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Liu, Chao, and Shouying Lin. "Research on Mini-EfficientDet Identification Algorithm Based on Transfer Learning." Journal of Physics: Conference Series 2218, no. 1 (March 1, 2022): 012039. http://dx.doi.org/10.1088/1742-6596/2218/1/012039.
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Abstract In this research paper, the Chinese beehive culture in Fujian Province, China is used as the carrier, and the Mini-EfficientDet deep neural network after migration is used to identify common species at the hive door, that is, the identification of Chinese bees, wasps and cockroaches in the form of nymphs. In this paper, we define the modified model as Mini-EfficientDet by compressing the initial EfficientDet model and adding the category imbalance function, which makes it focus more on the recognition and classification of small targets while ensuring the recognition accuracy. Through the test on the MSCOCO2017 data set, it is concluded that when the backbone network adopts EfficientNet B7, it shows strong detection accuracy in detecting targets of various scales, which confirms the role of the category imbalance function proposed in this paper and the efficiency of the improved EfficientDet model. Detection accuracy. The pre-trained model is transferred to the field of beehive species detection through migration learning, that is, after the post-training of the self-collected data set, the detection accuracy of Chinese bee, cockroach, and wasp is 98.66%, 83.71%, and 82.06%. It has made sufficient algorithmic preparations for the later detection and early warning system of beehive species invasion.
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Nawaz, Marriam, Tahira Nazir, Ali Javed, Usman Tariq, Hwan-Seung Yong, Muhammad Attique Khan, and Jaehyuk Cha. "An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization." Sensors 22, no. 2 (January 7, 2022): 434. http://dx.doi.org/10.3390/s22020434.
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Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.
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Ahmed, Syed Sohail, Zahid Mehmood, Imran Ahmad Awan, and Rehan Mehmood Yousaf. "A Novel Technique for Handwritten Digit Recognition Using Deep Learning." Journal of Sensors 2023 (January 30, 2023): 1–15. http://dx.doi.org/10.1155/2023/2753941.
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Handwritten digit recognition (HDR) shows a significant application in the area of information processing. However, correct recognition of such characters from images is a complicated task due to immense variations in the writing style of people. Moreover, the occurrence of several image artifacts like the existence of intensity variations, blurring, and noise complicates this process. In the proposed method, we have tried to overcome the aforementioned limitations by introducing a deep learning- (DL-) based technique, namely, EfficientDet-D4, for numeral categorization. Initially, the input images are annotated to exactly show the region of interest (ROI). In the next phase, these images are used to train the EfficientNet-B4-based EfficientDet-D4 model to detect and categorize the numerals into their respective classes from zero to nine. We have tested the proposed model over the MNIST dataset to demonstrate its efficacy and attained an average accuracy value of 99.83%. Furthermore, we have accomplished the cross-dataset evaluation on the USPS database and achieved an accuracy value of 99.10%. Both the visual and reported experimental results show that our method can accurately classify the HDR from images even with the varying writing style and under the presence of various sample artifacts like noise, blurring, chrominance, position, and size variations of numerals. Moreover, the introduced approach is capable of generalizing well to unseen cases which confirms that the EfficientDet-D4 model is an effective solution to numeral recognition.
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Saleh, Mubarak Auwalu, Zubaida Said Ameen, Chadi Altrjman, and Fadi Al-Turjman. "Computer-Vision-Based Statue Detection with Gaussian Smoothing Filter and EfficientDet." Sustainability 14, no. 18 (September 12, 2022): 11413. http://dx.doi.org/10.3390/su141811413.
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Smart tourism is a developing industry, and numerous nations are planning to establish smart cities in which technology is employed to make life easier and link nearly everything. Many researchers have created object detectors; however, there is a demand for lightweight versions that can fit into smartphones and other edge devices. The goal of this research is to demonstrate the notion of employing a mobile application that can detect statues efficiently on mobile applications, and also improve the performance of the models by employing the Gaussian Smoothing Filter (GSF). In this study, three object detection models, EfficientDet—D0, EfficientDet—D2 and EfficientDet—D4, were trained on original and smoothened images; moreover, their performance was compared to find a model efficient detection score that is easy to run on a mobile phone. EfficientDet—D4, trained on smoothened images, achieves a Mean Average Precision (mAP) of 0.811, an mAP-50 of 1 and an mAP-75 of 0.90.
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Wang, Yanfeng, Tao Wang, Xin Zhou, Weiwei Cai, Runmin Liu, Meigen Huang, Tian Jing, et al. "TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer." Computational Intelligence and Neuroscience 2022 (April 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/2262549.
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In recent years, analysis and optimization algorithm based on image data is a research hotspot. Aircraft detection based on aerial images can provide data support for accurately attacking military targets. Although many efforts have been devoted, it is still challenging due to the poor environment, the vastness of the sky background, and so on. This paper proposes an aircraft detection method named TransEffiDet in aerial images based on the EfficientDet method and Transformer module. We improved the EfficientDet algorithm by combining it with the Transformer which models the long-range dependency for the feature maps. Specifically, we first employ EfficientDet as the backbone network, which can efficiently fuse the different scale feature maps. Then, deformable Transformer is used to analyze the long-range correlation for global feature extraction. Furthermore, we designed a fusion module to fuse the long-range and short-range features extracted by EfficientDet and deformable Transformer, respectively. Finally, object class is produced by feeding the feature map to the class prediction net and the bounding box predictions are generated by feeding these fused features to the box prediction net. The mean Average Precision (mAP) is 86.6%, which outperforms the EfficientDet by 5.8%. The experiment shows that TransEffiDet is more robust than other methods. Additionally, we have established a public aerial dataset for aircraft detection, which will be released along with this paper.
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Liao, Jianhao, Jiayu Zou, Ao Shen, Jinfu Liu, and Xiaofei Du. "Cigarette end detection based on EfficientDet." Journal of Physics: Conference Series 1748 (January 2021): 062015. http://dx.doi.org/10.1088/1742-6596/1748/6/062015.
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Jia, Jiaqi, Min Fu, Xuefeng Liu, and Bing Zheng. "Underwater Object Detection Based on Improved EfficientDet." Remote Sensing 14, no. 18 (September 8, 2022): 4487. http://dx.doi.org/10.3390/rs14184487.
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Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. The Enhanced Feature Extraction module is constructed for multi-scale feature fusion to enhance the feature extraction ability of the network to different objects. The results of experiments demonstrate that the mean average precision (mAP) of the proposed method reaches 91.67% and 92.81% on the URPC dataset and the Kaggle dataset, respectively, which is better than other object detection models. At the same time, the processing speed reaches 37.5 frame per second (FPS) on the URPC dataset, which can meet the real-time requirements. It can provide a useful reference for underwater robots to perform tasks such as intelligent grasping.
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Nawaz, Marriam, Tahira Nazir, Jamel Baili, Muhammad Attique Khan, Ye Jin Kim, and Jae-Hyuk Cha. "CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model." Diagnostics 13, no. 2 (January 9, 2023): 248. http://dx.doi.org/10.3390/diagnostics13020248.
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The competence of machine learning approaches to carry out clinical expertise tasks has recently gained a lot of attention, particularly in the field of medical-imaging examination. Among the most frequently used clinical-imaging modalities in the healthcare profession is chest radiography, which calls for prompt reporting of the existence of potential anomalies and illness diagnostics in images. Automated frameworks for the recognition of chest abnormalities employing X-rays are being introduced in health departments. However, the reliable detection and classification of particular illnesses in chest X-ray samples is still a complicated issue because of the complex structure of radiographs, e.g., the large exposure dynamic range. Moreover, the incidence of various image artifacts and extensive inter- and intra-category resemblances further increases the difficulty of chest disease recognition procedures. The aim of this study was to resolve these existing problems. We propose a deep learning (DL) approach to the detection of chest abnormalities with the X-ray modality using the EfficientDet (CXray-EffDet) model. More clearly, we employed the EfficientNet-B0-based EfficientDet-D0 model to compute a reliable set of sample features and accomplish the detection and classification task by categorizing eight categories of chest abnormalities using X-ray images. The effective feature computation power of the CXray-EffDet model enhances the power of chest abnormality recognition due to its high recall rate, and it presents a lightweight and computationally robust approach. A large test of the model employing a standard database from the National Institutes of Health (NIH) was conducted to demonstrate the chest disease localization and categorization performance of the CXray-EffDet model. We attained an AUC score of 0.9080, along with an IOU of 0.834, which clearly determines the competency of the introduced model.
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Zheng, Xin, Feng Chen, Liming Lou, Pengle Cheng, and Ying Huang. "Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network." Remote Sensing 14, no. 3 (January 23, 2022): 536. http://dx.doi.org/10.3390/rs14030536.
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To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model. The YOLOv3 showed a detection speed up to 27 FPS, indicating it is a real-time smoke detector. By comparing these algorithms with the current existing forest fire smoke detection algorithms, it can be found that the deep convolutional neural network algorithms result in better smoke detection accuracy. In particular, the EfficientDet algorithm achieves an average detection accuracy of 95.7%, which is the best real-time forest fire smoke detection among the evaluated algorithms.
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Carmo, Diedre, Israel Campiotti, Lívia Rodrigues, Irene Fantini, Gustavo Pinheiro, Daniel Moraes, Rodrigo Nogueira, Leticia Rittner, and Roberto Lotufo. "Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals." Health Informatics Journal 27, no. 3 (July 2021): 146045822110330. http://dx.doi.org/10.1177/14604582211033017.
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The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.
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Yu, Byoungjoon, Kassahun Demissie Tola, Changgil Lee, and Seunghee Park. "Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique." Sensors 21, no. 21 (October 26, 2021): 7105. http://dx.doi.org/10.3390/s21217105.
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With the advent of the Fourth Industrial Revolution, the economic, social, and technological demands for pipe maintenance are increasing due to the aging of the infrastructure caused by the increase in industrial development and the expansion of cities. Owing to this, an automatic pipe damage detection system was built using a laser-scanned pipe’s ultrasonic wave propagation imaging (UWPI) data and conventional neural network (CNN)-based object detection algorithms. The algorithm used in this study was EfficientDet-d0, a CNN-based object detection algorithm which uses the transfer learning method. As a result, the mean average precision (mAP) was measured to be 0.39. The result found was higher than COCO EfficientDet-d0 mAP, which is expected to enable the efficient maintenance of piping used in construction and many industries.
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Song, Shaojun, Junfeng Jing, Yanqing Huang, and Mingyang Shi. "EfficientDet for fabric defect detection based on edge computing." Journal of Engineered Fibers and Fabrics 16 (January 2021): 155892502110083. http://dx.doi.org/10.1177/15589250211008346.
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The productivity of textile industry is positively correlated with the efficiency of fabric defect detection. Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods. Nonetheless, these cloud computing-based methods are still suboptimal due to the data transmission latency between the end devices and the cloud. To facilitate defect detection with more efficiency, a low-latency, low power consumption, easy upgrade, and automatical visual inspection system with the help of edge computing are proposed in this work. Firstly, the method uses EfficientDet-D0 as the detection algorithm, integrating the advantages of lightweight and scalable and can suit the resource-constrained edge device. Secondly, we performed data augmentations on five fabric datasets and verified the adaptability of the model in different types of fabrics. Finally, we transplanted the trained model to the edge device NVIDIA Jetson TX2 and optimized the model with TensorRT to make it detection faster. The performance of the proposed method is evaluated in five fabric datasets. The detection speed is up to 22.7 frame per second (FPS) on the edge device Jetson TX2. Compared with the cloud-based method, the response time is reduced by 2.5 times, with the capability of real-time industrial defect detection.
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Du, Yunben, Wei Lin, Weibo Zhong, and Yu Yuan. "An Effective Approach for Sonar Image Recognition with Improved Efficientdet and Ensemble Learning." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012038. http://dx.doi.org/10.1088/1742-6596/2258/1/012038.
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Abstract Precisely recognition of underwater targets plays a critical role in the field of underwater unmanned exploration. According to the characteristics of sonar image, in order to improve the accuracy of underwater automatic target recognition, this paper proposes an underwater target sonar image recognition method combined with improved Efficientdet and ensemble learning. According to the regional dominance principle and threshold segmentation method, the sonar image is preprocessed, and preliminary effective features extracted from preprocessed data by Efficientnet backbone, so as to balance the efficiency and power consumption of the algorithm. The feature fusion adopts the improved weighted bi-directional feature pyramid ( BiFPN ) structure to aggregate the global information and strengthen the feature representation of the shallow feature map, which optimizes the problem that the small feature extraction is not ideal due to the low resolution of the sonar image and less detail information. By building a two-level classifier to complete the learning task in an ensemble way, a better classification effect is obtained than a single classifier. This method is applied to the underwater intelligent perception group of the 10th National Marine Aircraft Design and Production Competition in 2021, and won the first prize in the country. The experimental results show that the designed ensemble network model is more accurate than the conventional convolution neural network in identifying underwater targets on the measured sonar image data set while ensuring a certain prediction speed.
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Xu, Renjie, Haifeng Lin, Kangjie Lu, Lin Cao, and Yunfei Liu. "A Forest Fire Detection System Based on Ensemble Learning." Forests 12, no. 2 (February 13, 2021): 217. http://dx.doi.org/10.3390/f12020217.
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Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.
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Munteanu, Dan, Diana Moina, Cristina Gabriela Zamfir, Ștefan Mihai Petrea, Dragos Sebastian Cristea, and Nicoleta Munteanu. "Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models." Sensors 22, no. 23 (December 6, 2022): 9536. http://dx.doi.org/10.3390/s22239536.
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In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.
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Abdul Manan, Amirul Asyraf, Mohd Azraai Mohd Razman, Ismail Mohd Khairuddin, and Muhammad Nur Aiman Shapiee. "Chili Plant Classification using Transfer Learning models through Object Detection." MEKATRONIKA 2, no. 2 (December 12, 2020): 23–27. http://dx.doi.org/10.15282/mekatronika.v2i2.6743.
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This study presents an application of using a Convolutional Neural Network (CNN) based detector to detect chili and its leaves in the chili plant image. Detecting chili on its plant is essential for the development of robotic vision and monitoring. Thus, helps us supervise the plant growth, furthermore, analyses their productivity and quality. This paper aims to develop a system that can monitor and identify bird’s eye chili plants by implementing machine learning. First, the development of methodology for efficient detection of bird’s eye chili and its leaf was made. A dataset of a total of 1866 images after augmentation of bird’s eye chili and its leaf was used in this experiment. YOLO Darknet was implemented to train the dataset. After a series of experiments were conducted, the model is compared with other transfer learning models like YOLO Tiny, Faster R-CNN, and EfficientDet. The classification performance of these transfer learning models has been calculated and compared with each other. The experimental result shows that the Yolov4 Darknet model achieves mAP of 75.69%, followed by EfficientDet at 71.85% for augmented dataset.
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Iancu, Bogdan, Valentin Soloviev, Luca Zelioli, and Johan Lilius. "ABOships—An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations." Remote Sensing 13, no. 5 (March 5, 2021): 988. http://dx.doi.org/10.3390/rs13050988.
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Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extractor and object size. Our experiments showed that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter.
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Sheng, Zhenwen, and Guiyun Wang. "Fast Method of Detecting Packaging Bottle Defects Based on ECA-EfficientDet." Journal of Sensors 2022 (February 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/9518910.
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Conventional methods of detecting packaging defects face challenges with multiobject simultaneous detection for automatic filling and packaging of food. Targeting this issue, we propose a packaging defect detection method based on the ECA-EfficientDet transfer learning algorithm. First, we increased the complexity in the sampled data using the mosaic data augmentation technique. Then, we introduced a channel-importance prediction mechanism and the Mish activation function and designed ECA-Convblock to improve the specificity in the feature extractions of the backbone network. Heterogeneous data transfer learning was then carried out on the optimized network to improve the generalization capability of the model on a small population of training data. We conducted performance testing and a comparative analysis of the trained model with defect data. The results indicate that, compared with other algorithms, our method achieves higher accuracy of 99.16% with good stability.
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Teimouri, Nima, Rasmus Nyholm Jørgensen, and Ole Green. "Novel Assessment of Region-Based CNNs for Detecting Monocot/Dicot Weeds in Dense Field Environments." Agronomy 12, no. 5 (May 12, 2022): 1167. http://dx.doi.org/10.3390/agronomy12051167.
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Weeding operations represent an effective approach to increase crop yields. Reliable and precise weed detection is a prerequisite for achieving high-precision weed monitoring and control in precision agriculture. To develop an effective approach for detecting weeds within the red, green, and blue (RGB) images, two state-of-the-art object detection models, EfficientDet (coefficient 3) and YOLOv5m, were trained on more than 26,000 in situ labeled images with monocot/dicot classes recorded from more than 200 different fields in Denmark. The dataset was collected using a high velocity camera (HVCAM) equipped with a xenon ring flash that overrules the sunlight and minimize shadows, which enables the camera to record images with a horizontal velocity of over 50 km h-1. Software-wise, a novel image processing algorithm was developed and utilized to generate synthetic images for testing the model performance on some difficult occluded images with weeds that were properly generated using the proposed algorithm. Both deep-learning networks were trained on in-situ images and then evaluated on both synthetic and new unseen in-situ images to assess their performances. The obtained average precision (AP) of both EfficientDet and YOLOv5 models on 6625 synthetic images were 64.27% and 63.23%, respectively, for the monocot class and 45.96% and 37.11% for the dicot class. These results confirmed that both deep-learning networks could detect weeds with high performance. However, it is essential to verify both the model’s robustness on in-situ images in which there is heavy occlusion with a complicated background. Therefore, 1149 in-field images were recorded in 5 different fields in Denmark and then utilized to evaluate both proposed model’s robustness. In the next step, by running both models on 1149 in-situ images, the AP of monocot/dicot for EfficientDet and YOLOv5 models obtained 27.43%/42.91% and 30.70%/51.50%, respectively. Furthermore, this paper provides information regarding challenges of monocot/dicot weed detection by releasing 1149 in situ test images with their corresponding labels (RoboWeedMap) publicly to facilitate the research in the weed detection domain within the precision agriculture field.
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Li, He, Peng Wang, and Chong Huang. "Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery." Remote Sensing 14, no. 13 (June 30, 2022): 3143. http://dx.doi.org/10.3390/rs14133143.
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With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.
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Zhu Shisong, 朱世松, 孙秀帅 Sun Xiushuai, 赵理山 Zhao Lishan, 芦碧波 Lu Bibo, and 姚东林 Yao Donglin. "基于改进EfficientDet的线束端子线芯检测算法." Laser & Optoelectronics Progress 59, no. 18 (2022): 1815008. http://dx.doi.org/10.3788/lop202259.1815008.
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Edvardsen, Isak Paasche, Anna Teterina, Thomas Johansen, Jonas Nordhaug Myhre, Fred Godtliebsen, and Napat Limchaichana Bolstad. "Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016." Journal of International Medical Research 50, no. 11 (November 2022): 030006052211351. http://dx.doi.org/10.1177/03000605221135147.
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Objective To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width. Methods Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental panoramic radiographs. Four pretrained object detectors were tested. We randomly chose 80% of the data for training and 20% for testing. Models were trained using GeForce RTX 2080 Ti with 11 GB GPU memory (NVIDIA Corporation, Santa Clara, CA, USA). Python programming language version 3.7 was used for analysis. Results The EfficientDet-D0 model showed the highest average precision of 0.30. When the threshold to regard a prediction as correct (intersection over union) was set to 0.5, the average precision was 0.79. The RetinaNet model achieved the lowest average precision of 0.23, and the precision was 0.64 when the intersection over union was set to 0.5. The procedure to estimate mandibular cortical width showed acceptable results. Of 100 random images, the algorithm produced an output 93 times, 20 of which were not visually satisfactory. Conclusions EfficientDet-D0 effectively detected the mental foramen. Methods for estimating bone quality are important in radiology and require further development.
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Ammar, Adel, Anis Koubaa, and Bilel Benjdira. "Deep-Learning-Based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images." Agronomy 11, no. 8 (July 22, 2021): 1458. http://dx.doi.org/10.3390/agronomy11081458.
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In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images.
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Ye, Yuanxin, Xiaoyue Ren, Bai Zhu, Tengfeng Tang, Xin Tan, Yang Gui, and Qin Yao. "An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images." Remote Sensing 14, no. 3 (January 21, 2022): 516. http://dx.doi.org/10.3390/rs14030516.
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For remote sensing object detection, fusing the optimal feature information automatically and overcoming the sensitivity to adapt multi-scale objects remains a significant challenge for the existing convolutional neural networks. Given this, we develop a convolutional network model with an adaptive attention fusion mechanism (AAFM). The model is proposed based on the backbone network of EfficientDet. Firstly, according to the characteristics of object distribution in datasets, the stitcher is applied to make one image containing objects of various scales. Such a process can effectively balance the proportion of multi-scale objects and handle the scale-variable properties. In addition, inspired by channel attention, a spatial attention model is also introduced in the construction of the adaptive attention fusion mechanism. In this mechanism, the semantic information of the different feature maps is obtained via convolution and different pooling operations. Then, the parallel spatial and channel attention are fused in the optimal proportions by the fusion factors to get the further representative feature information. Finally, the Complete Intersection over Union (CIoU) loss is used to make the bounding box better cover the ground truth. The experimental results of the optical image dataset DIOR demonstrate that, compared with state-of-the-art detectors such as the Single Shot multibox Detector (SSD), You Only Look Once (YOLO) v4, and EfficientDet, the proposed module improves accuracy and has stronger robustness.
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Qian, Jingjing, and Haifeng Lin. "A Forest Fire Identification System Based on Weighted Fusion Algorithm." Forests 13, no. 8 (August 16, 2022): 1301. http://dx.doi.org/10.3390/f13081301.
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The occurrence of forest fires causes serious damage to ecological diversity and the safety of people’s property and life. However, due to the complex forest environment, the changeable shape of forest fires, and the uncertainty of flame color and texture, forest fire detection becomes very difficult. Traditional image processing methods rely heavily on artificial features and are not generally applicable to different forest fire scenes. In order to solve the problem of inaccurate forest fire recognition caused by the manual extraction of features, some scholars use deep learning technology to adaptively learn and extract forest fire features, but they often use a single target detection model, and their lack of learning and perception makes it difficult for them to accurately identify forest fires in a complex forest fire environment. Therefore, in order to overcome the shortcomings of the manual extraction of features and achieve a higher accuracy of forest fire recognition, this paper proposes an algorithm based on weighted fusion to identify forest fire sources in different scenarios, fuses two independent weakly supervised models Yolov5 and EfficientDet, completes the training and prediction of data sets in parallel, and uses the weighted boxes fusion algorithm (WBF) to process the prediction results to obtain the fusion frame. Finally, the model is evaluated by Microsoft COCO standard. Experimental results show that compared with Yolov5 and EfficientDet, the proposed Y4SED improves the detection performance by 2.5% to 4.5%. The fused algorithm proposed in this paper has better feature extraction ability, can extract more forest fire feature information, and better balances the recognition accuracy and complexity of the model, which provides a reference for forest fire target detection in the real environment.
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Tseng, Hsin-Hung, Ming-Der Yang, R. Saminathan, Yu-Chun Hsu, Chin-Ying Yang, and Dong-Hong Wu. "Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning." Remote Sensing 14, no. 12 (June 13, 2022): 2837. http://dx.doi.org/10.3390/rs14122837.
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To meet demand for agriculture products, researchers have recently focused on precision agriculture to increase crop production with less input. Crop detection based on computer vision with unmanned aerial vehicle (UAV)-acquired images plays a vital role in precision agriculture. In recent years, machine learning has been successfully applied in image processing for classification, detection and segmentation. Accordingly, the aim of this study is to detect rice seedlings in paddy fields using transfer learning from two machine learning models, EfficientDet-D0 and Faster R-CNN, and to compare the results to the legacy approach—histograms of oriented gradients (HOG)-based support vector machine (SVM) classification. This study relies on a significant UAV image dataset to build a model to detect tiny rice seedlings. The HOG-SVM classifier was trained and achieved an F1-score of 99% in both training and testing. The performance of HOG-SVM, EfficientDet and Faster R-CNN models, respectively, were measured in mean average precision (mAP), with 70.0%, 95.5% and almost 100% in training and 70.2%, 83.2% and 88.8% in testing, and mean Intersection-over-Union (mIoU), with 46.5%, 67.6% and 99.6% in training and 46.6%, 57.5% and 63.7% in testing. The three models were also measured with three additional datasets acquired on different dates to evaluate model applicability with various imaging conditions. The results demonstrate that both CNN-based models outperform HOG-SVM, with a 10% higher mAP and mIoU. Further, computation speed is at least 1000 times faster than that of HOG-SVM with sliding window. Overall, the adoption of transfer learning allows for rapid establishment of object detection applications with promising performance.
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Nakaguchi, Victor Massaki, and Tofael Ahamed. "Fast and Non-Destructive Quail Egg Freshness Assessment Using a Thermal Camera and Deep Learning-Based Air Cell Detection Algorithms for the Revalidation of the Expiration Date of Eggs." Sensors 22, no. 20 (October 11, 2022): 7703. http://dx.doi.org/10.3390/s22207703.
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Freshness is one of the most important parameters for assessing the quality of avian eggs. Available techniques to estimate the degradation of albumen and enlargement of the air cell are either destructive or not suitable for high-throughput applications. The aim of this research was to introduce a new approach to evaluate the air cell of quail eggs for freshness assessment as a fast, noninvasive, and nondestructive method. A new methodology was proposed by using a thermal microcamera and deep learning object detection algorithms. To evaluate the new method, we stored 174 quail eggs and collected thermal images 30, 50, and 60 days after the labeled expiration date. These data, 522 in total, were expanded to 3610 by image augmentation techniques and then split into training and validation samples to produce models of the deep learning algorithms, referred to as “You Only Look Once” version 4 and 5 (YOLOv4 and YOLOv5) and EfficientDet. We tested the models in a new dataset composed of 60 eggs that were kept for 15 days after the labeled expiration label date. The validation of our methodology was performed by measuring the air cell area highlighted in the thermal images at the pixel level; thus, we compared the difference in the weight of eggs between the first day of storage and after 10 days under accelerated aging conditions. The statistical significance showed that the two variables (air cell and weight) were negatively correlated (R2 = 0.676). The deep learning models could predict freshness with F1 scores of 0.69, 0.89, and 0.86 for the YOLOv4, YOLOv5, and EfficientDet models, respectively. The new methodology for freshness assessment demonstrated that the best model reclassified 48.33% of our testing dataset. Therefore, those expired eggs could have their expiration date extended for another 2 weeks from the original label date.
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Zhao, Kaixuan, Ruihong Zhang, and Jiangtao Ji. "A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image." Sensors 21, no. 20 (October 11, 2021): 6734. http://dx.doi.org/10.3390/s21206734.
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In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag.
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Anuar, Mohamed Marzhar, Alfian Abdul Halin, Thinagaran Perumal, and Bahareh Kalantar. "Aerial Imagery Paddy Seedlings Inspection Using Deep Learning." Remote Sensing 14, no. 2 (January 7, 2022): 274. http://dx.doi.org/10.3390/rs14020274.
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In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the capability of recent advances in computing architectures could become a new alternative to existing solutions. Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. Optimal planting density is a crucial factor for paddy cultivation as it will influence the quality and quantity of production. There have been several studies involving planting density using computer vision and remote sensing approaches. While most of the studies have shown promising results, they have disadvantages and show room for improvement. One of the disadvantages is that the studies aim to detect and count all the paddy seedlings to determine planting density. The defective paddy seedlings’ locations are not pointed out to help farmers during the sowing process. In this work we aimed to explore several deep convolutional neural networks (DCNN) models to determine which one performs the best for defective paddy seedling detection using aerial imagery. Thus, we evaluated the accuracy, robustness, and inference latency of one- and two-stage pretrained object detectors combined with state-of-the-art feature extractors such as EfficientNet, ResNet50, and MobilenetV2 as a backbone. We also investigated the effect of transfer learning with fine-tuning on the performance of the aforementioned pretrained models. Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.
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Yan, Bin, Pan Fan, Xiaoyan Lei, Zhijie Liu, and Fuzeng Yang. "A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5." Remote Sensing 13, no. 9 (April 21, 2021): 1619. http://dx.doi.org/10.3390/rs13091619.
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The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.
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Luo, Yu, Yifan Zhang, Xize Sun, Hengwei Dai, and Xiaohui Chen. "Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50." Journal of Healthcare Engineering 2021 (October 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/2267635.
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Computer-aided diagnosis (CAD) has nearly fifty years of history and has assisted many clinicians in the diagnosis. With the development of technology, recently, researches use the deep learning method to get high accuracy results in the CAD system. With CAD, the computer output can be used as a second choice for radiologists and contribute to doctors doing the final right decisions. Chest abnormality detection is a classic detection and classification problem; researchers need to classify common thoracic lung diseases and localize critical findings. For the detection problem, there are two deep learning methods: one-stage method and two-stage method. In our paper, we introduce and analyze some representative model, such as RCNN, SSD, and YOLO series. In order to better solve the problem of chest abnormality detection, we proposed a new model based on YOLOv5 and ResNet50. YOLOv5 is the latest YOLO series, which is more flexible than the one-stage detection algorithms before. The function of YOLOv5 in our paper is to localize the abnormality region. On the other hand, we use ResNet, avoiding gradient explosion problems in deep learning for classification. And we filter the result we got from YOLOv5 and ResNet. If ResNet recognizes that the image is not abnormal, the YOLOv5 detection result is discarded. The dataset is collected via VinBigData’s web-based platform, VinLab. We train our model on the dataset using Pytorch frame and use the mAP, precision, and F1-score as the metrics to evaluate our model’s performance. In the progress of experiments, our method achieves superior performance over the other classical approaches on the same dataset. The experiments show that YOLOv5’s mAP is 0.010, 0.020, 0.023 higher than those of YOLOv5, Fast RCNN, and EfficientDet. In addition, in the dimension of precision, our model also performs better than other models. The precision of our model is 0.512, which is 0.018, 0.027, 0.033 higher than YOLOv5, Fast RCNN, and EfficientDet.
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Wu, Chenrui, Long Chen, and Shiqing Wu. "A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects’ 6D Pose Estimation." Applied Sciences 11, no. 22 (November 9, 2021): 10531. http://dx.doi.org/10.3390/app112210531.
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6D pose estimation of objects is essential for intelligent manufacturing. Current methods mainly place emphasis on the single object’s pose estimation, which limit its use in real-world applications. In this paper, we propose a multi-instance framework of 6D pose estimation for textureless objects in an industrial environment. We use a two-stage pipeline for this purpose. In the detection stage, EfficientDet is used to detect target instances from the image. In the pose estimation stage, the cropped images are first interpolated into a fixed size, then fed into a pseudo-siamese graph matching network to calculate dense point correspondences. A modified circle loss is defined to measure the differences of positive and negative correspondences. Experiments on the antenna support demonstrate the effectiveness and advantages of our proposed method.
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Booysens, Aimee, and Serestina Viriri. "Exploration of Ear Biometrics Using EfficientNet." Computational Intelligence and Neuroscience 2022 (August 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/3514807.
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Biometrics is the recognition of a human using biometric characteristics for identification, which may be physiological or behavioral. The physiological biometric features are the face, ear, iris, fingerprint, and handprint; behavioral biometrics are signatures, voice, gait pattern, and keystrokes. Numerous systems have been developed to distinguish biometric traits used in multiple applications, such as forensic investigations and security systems. With the current worldwide pandemic, facial identification has failed due to users wearing masks; however, the human ear has proven more suitable as it is visible. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet. This paper presents the performance achieved in this research and shows the efficiency of EfficientNet on ear recognition. The nine variants of EfficientNets were fine-tuned and implemented on multiple publicly available ear datasets. The experiments showed that EfficientNet variant B8 achieved the best accuracy of 98.45%.
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Yang, Qing, Shukai Duan, and Lidan Wang. "Efficient Identification of Apple Leaf Diseases in the Wild Using Convolutional Neural Networks." Agronomy 12, no. 11 (November 9, 2022): 2784. http://dx.doi.org/10.3390/agronomy12112784.
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Efficient identification of apple leaf diseases (ALDs) can reduce the use of pesticides and increase the quality of apple fruit, which is of significance to smart agriculture. However, existing research into identifying ALDs lacks models/methods that satisfy efficient identification in the wild environment, hindering the application of smart agriculture in the apple industry. Therefore, this paper explores an ACCURATE, LIGHTWEIGHT, and ROBUST convolutional neural network (CNN) called EfficientNet-MG, improving the conventional EfficientNet network by the multistage feature fusion (MSFF) method and gaussian error linear unit (GELU) activation function. The shallow and deep convolutional layers usually contain detailed and semantic information, respectively, but conventional EfficientNets do not fully utilize the different stage convolutional layers. Thus, MSFF was adopted to improve the semantic representation capacity of the last layer of features, and GELU was used to adapt to complicated tasks. Further, a comprehensive ALD dataset called AppleLeaf9 was constructed for the wild environment. The experimental results show that EfficientNet-MG achieves a higher accuracy (99.11%) and fewer parameters (8.42 M) than the five classical CNN models, thus proving that EfficientNet-MG achieves more competitive results on ALD identification.
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You, Tengfei, Weiyang Chen, Haifeng Wang, Yang Yang, and Xinang Liu. "Automatic Garbage Scattered Area Detection with Data Augmentation and Transfer Learning in SUAV Low-Altitude Remote Sensing Images." Mathematical Problems in Engineering 2020 (October 19, 2020): 1–13. http://dx.doi.org/10.1155/2020/7307629.
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Cleaning up the garbage timely plays an important role in protecting the ecological environment of nature reserves. The traditional approach adopts manual patrol and centralized cleaning to clean up garbage, which is inefficient. In order to protect the ecological environment of nature reserves, this paper proposes an automatic garbage scattered area detection (GSAD) model based on the state-of-the-art deep learning EfficientDet method, transfer learning, data augmentation, and image blocking. The main contributions of this paper are (1) we build a garbage sample dataset based on small unmanned aerial vehicle (SUAV) low-altitude remote sensing and (2) we propose a novel data augmentation approach based on garbage scattered area detection and (3) this paper establishes a model (GSAD) for garbage scattered area detection based on data augmentation, transfer learning, and image blocking and gives future research directions. Experimental results show that the GSAD model can achieve the F1-score of 95.11% and average detection time of 1.096 s.
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Yu, Hongxia, Lijun Yun, Zaiqing Chen, Feiyan Cheng, and Chunjie Zhang. "A Small Object Detection Algorithm Based on Modulated Deformable Convolution and Large Kernel Convolution." Computational Intelligence and Neuroscience 2023 (January 24, 2023): 1–10. http://dx.doi.org/10.1155/2023/2506274.
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Object detection is one of the most critical areas in computer vision, and it plays an essential role in a variety of practice scenarios. However, small object detection has always been a key and difficult problem in the field of object detection. Therefore, considering the balance between the effectiveness and efficiency of the small object detection algorithm, this study proposes an improved YOLOX detection algorithm (BGD-YOLOX) to improve the detection effect of small objects. We present the BigGhost module, which combines the Ghost model with a modulated deformable convolution to optimize the YOLOX for greater accuracy. At the same time, it can reduce the inference time by reducing the number of parameters and the amount of computation. The experimental results show that BGD-YOLOX has a higher average accuracy rate in terms of small target detection, with mAP0.5 up to 88.3% and mAP0.95 up to 56.7%, which surpasses the most advanced object detection algorithms such as EfficientDet, CenterNet, and YOLOv4.
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Munien, Chanaleä, and Serestina Viriri. "Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets." Computational Intelligence and Neuroscience 2021 (April 9, 2021): 1–17. http://dx.doi.org/10.1155/2021/5580914.
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Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.
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Yang, Cheng-Hong, Jai-Hong Ren, Hsiu-Chen Huang, Li-Yeh Chuang, and Po-Yin Chang. "Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion." Computational Intelligence and Neuroscience 2021 (November 8, 2021): 1–15. http://dx.doi.org/10.1155/2021/9409508.
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Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.
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Li, Jie, Zhixing Wang, Bo Qi, Jianlin Zhang, and Hu Yang. "MEMe: A Mutually Enhanced Modeling Method for Efficient and Effective Human Pose Estimation." Sensors 22, no. 2 (January 14, 2022): 632. http://dx.doi.org/10.3390/s22020632.
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In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.
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An, Qing, Shisong Wu, Ruizhe Shi, Haojun Wang, Jun Yu, and Zhifeng Li. "Intelligent Detection of Hazardous Goods Vehicles and Determination of Risk Grade Based on Deep Learning." Sensors 22, no. 19 (September 20, 2022): 7123. http://dx.doi.org/10.3390/s22197123.
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Currently, deep learning has been widely applied in the field of object detection, and some relevant scholars have applied it to vehicle detection. In this paper, the deep learning EfficientDet model is analyzed, and the advantages of the model in the detection of hazardous good vehicles are determined. The adaptive training model is built based on the optimization of the training process, and the training model is used to detect hazardous goods vehicles. The detection results are compared with Cascade R-CNN and CenterNet, and the results show that the proposed method is superior to the other two methods in two aspects of computational complexity and detection accuracy. Simultaneously, the proposed method is suitable for the detection of hazardous goods vehicles in different scenarios. We make statistics on the number of detected hazardous goods vehicles at different times and places. The risk grade of different locations is determined according to the statistical results. Finally, the case study shows that the proposed method can be used to detect hazardous goods vehicles and determine the risk level of different places.
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Siddiqui, Asra Abid, Usman Zabit, and Olivier D. Bernal. "Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using Deep Neural Networks." Sensors 22, no. 24 (December 14, 2022): 9831. http://dx.doi.org/10.3390/s22249831.
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Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariably happens due to optical speckle encountered when sensing the motion of non-cooperative remote target surfaces. In this work, deep neural networks have been trained under variable optical feedback conditions so that interferometric fringe detection and corresponding displacement measurement can be achieved. We have also proposed a method for automatic labelling of SMI fringes under variable optical feedback to facilitate the generation of a large training dataset. Specifically, we have trained two deep neural network models, namely Yolov5 and EfficientDet, and analysed the performance of these networks on various experimental SMI signals acquired by using different laser-diode-based sensors operating under different noise and speckle conditions. The performance has been quantified in terms of fringe detection accuracy, signal to noise ratio, depth of modulation, and execution time parameters. The impact of network architecture on real-time sensing is also discussed.
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AlNujaidi, Khalid, and Ghadah AlHabib. "Computer Vision For a Camel-Vehicle Collision Mitigation System." International Journal on Cybernetics & Informatics 12, no. 1 (January 30, 2023): 1–9. http://dx.doi.org/10.5121/ijci.2023.120111.
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As the population grows and more land is being used for urbanization, ecosystems are disrupted by our roads and cars. This expansion of infrastructure cuts through wildlife territories, leading to many instances of Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue that is having a global socio-economic impact, resulting in billions of dollars in property damage and, at times, fatalities for vehicle occupants. In Saudi Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC) being particularly deadly due to the large size of camels, which results in a 25% fatality rate [4]. The focus of this work is to test different object detection models on the task of detecting camels on the road. The Deep Learning (DL) object detection models used in the experiments are: CenterNet, EfficientDet, Faster R-CNN, and SSD. Results of the experiments show that CenterNet performed the best in terms of accuracy and was the most efficient in training. In the future, the plan is to expand on this work by developing a system to make countryside roads safer.
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Oloko-Oba, Mustapha, and Serestina Viriri. "Ensemble of EfficientNets for the Diagnosis of Tuberculosis." Computational Intelligence and Neuroscience 2021 (December 14, 2021): 1–12. http://dx.doi.org/10.1155/2021/9790894.
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Tuberculosis (TB) remains a life-threatening disease and is one of the leading causes of mortality in developing regions due to poverty and inadequate medical resources. Tuberculosis is medicable, but it necessitates early diagnosis through reliable screening techniques. Chest X-ray is a recommended screening procedure for identifying pulmonary abnormalities. Still, this recommendation is not enough without experienced radiologists to interpret the screening results, which forms part of the problems in rural communities. Consequently, various computer-aided diagnostic systems have been developed for the automatic detection of tuberculosis. However, their sensitivity and accuracy are still significant challenges that require constant improvement due to the severity of the disease. Hence, this study explores the application of a leading state-of-the-art convolutional neural network (EfficientNets) model for the classification of tuberculosis. Precisely, five variants of EfficientNets were fine-tuned and implemented on two prominent and publicly available chest X-ray datasets (Montgomery and Shenzhen). The experiments performed show that EfficientNet-B4 achieved the best accuracy of 92.33% and 94.35% on both datasets. These results were then improved through Ensemble learning and reached 97.44%. The performance recorded in this study portrays the efficiency of fine-tuning EfficientNets on medical imaging classification through Ensemble.
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Jabir, Brahim, Noureddine Falih, and Khalid Rahmani. "Accuracy and Efficiency Comparison of Object Detection Open-Source Models." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 05 (May 20, 2021): 165. http://dx.doi.org/10.3991/ijoe.v17i05.21833.
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In agriculture, weeds cause direct damage to the crop, and it primarily affects the crop yield potential. Manual and mechanical weeding methods consume a lot of energy and time and do not give efficient results. Chemical weed control is still the best way to control weeds. However, the widespread and large-scale use of herbicides is harmful to the environment. Our study's objective is to propose an efficient model for a smart system to detect weeds in crops in real-time using computer vision. Our experiment dataset contains images of two different weed species well known in our region strained in this region with a temperate climate. The first is the Phalaris Paradoxa. The second is Convolvulus, manually captured with a professional camera from fields under different lighting conditions (from morning to afternoon in sunny and cloudy weather). The detection of weed and crop has experimented with four recent pre-configured open-source computer vision models for object detection: Detectron2, EfficientDet, YOLO, and Faster R-CNN. The performance comparison of weed detection models is executed on the Open CV and Keras platform using python language.
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Jung, Minji, Heekyung Yang, and Kyungha Min. "Improving Deep Object Detection Algorithms for Game Scenes." Electronics 10, no. 20 (October 17, 2021): 2527. http://dx.doi.org/10.3390/electronics10202527.
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The advancement and popularity of computer games make game scene analysis one of the most interesting research topics in the computer vision society. Among the various computer vision techniques, we employ object detection algorithms for the analysis, since they can both recognize and localize objects in a scene. However, applying the existing object detection algorithms for analyzing game scenes does not guarantee a desired performance, since the algorithms are trained using datasets collected from the real world. In order to achieve a desired performance for analyzing game scenes, we built a dataset by collecting game scenes and retrained the object detection algorithms pre-trained with the datasets from the real world. We selected five object detection algorithms, namely YOLOv3, Faster R-CNN, SSD, FPN and EfficientDet, and eight games from various game genres including first-person shooting, role-playing, sports, and driving. PascalVOC and MS COCO were employed for the pre-training of the object detection algorithms. We proved the improvement in the performance that comes from our strategy in two aspects: recognition and localization. The improvement in recognition performance was measured using mean average precision (mAP) and the improvement in localization using intersection over union (IoU).
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Luo, Ru, Lifu Chen, Jin Xing, Zhihui Yuan, Siyu Tan, Xingmin Cai, and Jielan Wang. "A Fast Aircraft Detection Method for SAR Images Based on Efficient Bidirectional Path Aggregated Attention Network." Remote Sensing 13, no. 15 (July 27, 2021): 2940. http://dx.doi.org/10.3390/rs13152940.
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In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient Bidirectional Path Aggregation Attention Network (EBPA2N) is proposed. In EBPA2N, YOLOv5s is used as the base network and then the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module are proposed and systematically integrated to improve the detection accuracy of the aircraft. The IEPA module aims to effectively extract advanced semantic and spatial information to better capture multi-scale scattering features of aircraft. Then, the lightweight ERSA module further enhances the extracted features to overcome the interference of complex background and speckle noise, so as to reduce false alarms. To verify the effectiveness of the proposed network, Gaofen-3 airports SAR data with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EBPA2N algorithm are 93.05% and 4.49%, respectively, which is superior to the latest networks of EfficientDet-D0 and YOLOv5s, and it also has an advantage of detection speed.
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Kim, Minki, Sunwon Kang, and Byoung-Dai Lee. "Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks." Sensors 22, no. 2 (January 14, 2022): 650. http://dx.doi.org/10.3390/s22020650.
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Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.
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Awaludin, Iwan, Trisna Gelar, Muhammad Rizqi Sholahuddin, Gina Melinia, Irvan Kadhafi, and Rezky Wahyuda Sitepu. "Dataset Citra Papan Sirkuit Tercetak dengan Komponen yang Terbakar." Building of Informatics, Technology and Science (BITS) 3, no. 3 (December 31, 2021): 179–85. http://dx.doi.org/10.47065/bits.v3i3.1025.
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The application of artificial intelligence, especially in the automatic optical inspection of printed circuit boards or PCBs, is increasingly being carried out by researchers. Unfortunately, the data used to train and test artificial intelligence models is synthetic data. Printed circuit boards in good condition are imaged and then changed by software to give the impression of defects. In addition, the type of damage is limited to pre-operation, namely when the PCB is not yet operational. After the PCB is operational, damage can occur, for example, burned components. Until now, there is no data set of PCB images with burned components. This study, therefore, explores data retrieval techniques that can produce the required data set. This data collection technique includes hardware setup and PCB data sources. Based on the exploration results, it is concluded that a trinocular digital microscope with high resolution can produce sharp PCB images. The obstacle that arises is the difficulty of getting PCBs with burned components. The solution was obtained by referring to the PCB repair video from the Youtube channel. Several data were collected and tested with EfficientDet with 90% mAP.
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Mikhaylov, Andrey Anatolievitch. "Automatic data labeling for document image segmentation using deep neural networks." Proceedings of the Institute for System Programming of the RAS 34, no. 6 (2022): 137–46. http://dx.doi.org/10.15514/ispras-2022-34(6)-10.
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The article proposes a new method for automatic data annotation for solving the problem of document image segmentation using deep object detection neural networks. The format of marked PDF files is considered as the initial data for markup. The peculiarity of this format is that it includes hidden marks that describe the logical and physical structure of the document. To extract them, a tool has been developed that simulates the operation of a stack-based printing machine according to the PDF format specification. For each page of the document, an image and annotation are generated in PASCAL VOC format. The classes and coordinates of the bounding boxes are calculated during the interpretation of the labeled PDF file based on the labels. To test the method, a collection of marked up PDF files was formed from which images of document pages and annotations for three segmentation classes (text, table, figure) were automatically obtained. Based on these data, a neural network of the EfficientDet D2 architecture was trained. The model was tested on manually labeled data from the same domain, which confirmed the effectiveness of using automatically generated data for solving applied problems.
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Ji, Wei, Yu Pan, Bo Xu, and Juncheng Wang. "A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX." Agriculture 12, no. 6 (June 13, 2022): 856. http://dx.doi.org/10.3390/agriculture12060856.
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In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the lightweight network Shufflenetv2 added with the convolutional block attention module (CBAM) as the backbone. An adaptive spatial feature fusion (ASFF) module is added to the PANet network to improve the detection accuracy, and only two extraction layers are used to simplify the network structure. The average precision (AP), precision, recall, and F1 of the trained network under the verification set are 96.76%, 95.62%, 93.75%, and 0.95, respectively, and the detection speed reaches 65 frames per second (FPS). The test results show that the AP value of Shufflenetv2-YOLOX is increased by 6.24% compared with YOLOX-Tiny, and the detection speed is increased by 18%. At the same time, it has a better detection effect and speed than the advanced lightweight networks YOLOv5-s, Efficientdet-d0, YOLOv4-Tiny, and Mobilenet-YOLOv4-Lite. Meanwhile, the half-precision floating-point (FP16) accuracy model on the embedded device Jetson Nano with TensorRT acceleration can reach 26.3 FPS. This method can provide an effective solution for the vision system of the apple picking robot.