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1
Mao, Yitong. "A pedestrian detection algorithm for low light and dense crowd Based on improved YOLO algorithm." MATEC Web of Conferences 355 (2022): 03020. http://dx.doi.org/10.1051/matecconf/202235503020.
Abstract:
The real-time pedestrian detection algorithm requires the model to be lightweight and robust. At the same time, the pedestrian object detection problem has the characteristics of aerial view Angle shooting, object overlap and weak light, etc. In order to design a more robust real-time detection model in weak light and crowded scene, this paper based on YOLO, raised a more efficient convolutional network. The experimental results show that, compared with YOLOX Network, the improved YOLO Network has a better detection effect in the lack of light scene and dense crowd scene, has a 5.0% advantage over YOLOX-s for pedestrians AP index, and has a 44.2% advantage over YOLOX-s for fps index.
2
Deng, Xiangwu, Long Qi, Zhuwen Liu, Song Liang, Kunsong Gong, and Guangjun Qiu. "Weed target detection at seedling stage in paddy fields based on YOLOX." PLOS ONE 18, no. 12 (December 13, 2023): e0294709. http://dx.doi.org/10.1371/journal.pone.0294709.
Abstract:
Weeds are one of the greatest threats to the growth of rice, and the loss of crops is greater in the early stage of rice growth. Traditional large-area spraying cannot selectively spray weeds and can easily cause herbicide waste and environmental pollution. To realize the transformation from large-area spraying to precision spraying in rice fields, it is necessary to quickly and efficiently detect the distribution of weeds. Benefiting from the rapid development of vision technology and deep learning, this study applies a computer vision method based on deep-learning-driven rice field weed target detection. To address the need to identify small dense targets at the rice seedling stage in paddy fields, this study propose a method for weed target detection based on YOLOX, which is composed of a CSPDarknet backbone network, a feature pyramid network (FPN) enhanced feature extraction network and a YOLO Head detector. The CSPDarknet backbone network extracts feature layers with dimensions of 80 pixels ⊆ 80 pixels, 40 pixels ⊆ 40 pixels and 20 pixels ⊆ 20 pixels. The FPN fuses the features from these three scales, and YOLO Head realizes the regression of the object classification and prediction boxes. In performance comparisons of different models, including YOLOv3, YOLOv4-tiny, YOLOv5-s, SSD and several models of the YOLOX series, namely, YOLOX-s, YOLOX-m, YOLOX-nano, and YOLOX-tiny, the results show that the YOLOX-tiny model performs best. The mAP, F1, and recall values from the YOLOX-tiny model are 0.980, 0.95, and 0.983, respectively. Meanwhile, the intermediate variable memory generated during the model calculation of YOLOX-tiny is only 259.62 MB, making it suitable for deployment in intelligent agricultural devices. However, although the YOLOX-tiny model is the best on the dataset in this paper, this is not true in general. The experimental results suggest that the method proposed in this paper can improve the model performance for the small target detection of sheltered weeds and dense weeds at the rice seedling stage in paddy fields. A weed target detection model suitable for embedded computing platforms is obtained by comparing different single-stage target detection models, thereby laying a foundation for the realization of unmanned targeted herbicide spraying performed by agricultural robots.
3
Li, Quanyang, Zhongqiang Luo, Xiangjie He, and Hongbo Chen. "LA_YOLOx: Effective Model to Detect the Surface Defects of Insulative Baffles." Electronics 12, no. 9 (April 27, 2023): 2035. http://dx.doi.org/10.3390/electronics12092035.
Abstract:
In the field of industry, defect detection based on YOLO models is widely used. In real detection, the method of defect detection of insulative baffles is artificial detection. The work efficiency of this method, however, is low because the detection is depends absolutely on human eyes. Considering the excellent performance of YOLOx, an intelligent detection method based on YOLOx is proposed. First, we selected a CIOU loss function instead of an IOU loss function by analyzing the defect characteristics of insulative baffles. In addition, considering the limitation of model resources in application scenarios, the lightweight YOLOx model is proposed. We replaced YOLOx’s backbone with lightweight backbones (MobileNetV3 and GhostNet), and used Depthwise separable convolution instead of conventional convolution. This operation reduces the number of network parameters by about 42% compared with the original YOLOx network. However, the mAP of it is decreased by about 0.8% compared with the original YOLOx model. Finally, the attention mechanism is introduced into the feature fusion module to solve this problem, and we called the lightweight YOLOx with an attention module LA_YOLOx. The final value of mAP of LA_YOLOx reaches 95.60%, while the original YOLOx model is 95.31%, which proves the effectiveness of the LA_YOLOx model.
4
Zhang, Yao, Ke Jiong Shen, Zhen Fang He, and Zhi Song Pan. "YOLO-infrared: Enhancing YOLOX for Infrared Scene." Journal of Physics: Conference Series 2405, no. 1 (December 1, 2022): 012015. http://dx.doi.org/10.1088/1742-6596/2405/1/012015.
Abstract:
Abstract Draw a bead on the specialty of infrared scenes and tackling the disequilibrium between positive and negative samples in object detectors, this paper introduces an object detection model for infrared scenes named YOLO-infrared based on YOLOX. This paper first analyses the shortcomings of the object detection model designed for the visible domain when applied to the infrared domain by visualizing the feature heat map of the YOLOX neck network. Considering the blurred edges of the target in the infrared image, which almost blends with the background in terms of colour and texture, with little distinction between pixel points at close distances, this paper first employs an attention module to extract the position relationship between distant pixels, which enhances the feature abstracting capacity of YOLOX. Finally, DR Loss is utilized to address the issue of positive and negative sample imbalance in the object detection process. The model YOLO-infrared proposed in this paper achieves 38.2% and 38.6% on the FLIR and KAIST datasets, respectively, which is at least 0.5% higher than the current SOAT detector.
5
Wang, Yaxin, Xinyuan Liu, Fanzhen Wang, Dongyue Ren, Yang Li, Zhimin Mu, Shide Li, and Yongcheng Jiang. "Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection." Sustainability 15, no. 19 (October 3, 2023): 14437. http://dx.doi.org/10.3390/su151914437.
Abstract:
Fuel types are essential for the control systems of briquette biofuel boilers, as the optimal combustion condition varies with fuel type. Moreover, the use of coal in biomass boilers is illegal in China, and the detection of coals will, in time, provide effective information for environmental supervision. This study established a briquette biofuel identification method based on the object detection of fuel images, including straw pellets, straw blocks, wood pellets, wood blocks, and coal. The YoloX-S model was used as the baseline network, and the proposed model in this study improved the detection performance by adding the self-attention mechanism module. The improved YoloX-S model showed better accuracy than the Yolo-L, YoloX-S, Yolov5, Yolov7, and Yolov8 models. The experimental results regarding fuel identification show that the improved model can effectively distinguish biomass fuel from coal and overcome false and missed detections found in the recognition of straw pellets and wood pellets by the original YoloX model. However, the interference of the complex background can greatly reduce the confidence of the object detection method using the improved YoloX-S model.
6
Ashraf, Imran, Soojung Hur, Gunzung Kim, and Yongwan Park. "Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions." Sensors 24, no. 2 (January 14, 2024): 522. http://dx.doi.org/10.3390/s24020522.
Abstract:
Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance.
7
Raimundo, António, João Pedro Pavia, Pedro Sebastião, and Octavian Postolache. "YOLOX-Ray: An Efficient Attention-Based Single-Staged Object Detector Tailored for Industrial Inspections." Sensors 23, no. 10 (May 11, 2023): 4681. http://dx.doi.org/10.3390/s23104681.
Abstract:
Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50 values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.
8
Слюсар, Вадим Іванович Слюсар. "Нейромережний метод підводного виявлення боєприпасів, що не спрацювали." Известия высших учебных заведений. Радиоэлектроника 65, no. 12 (December 26, 2022): 766–77. http://dx.doi.org/10.20535/s0021347023030020.
Abstract:
В статье обоснованы предложения по применению нейронных сетей семейства YOLO для обнаружения не сработавших подводных боеприпасов. При этом были использованы предварительно обученные на датасете MS COCO нейронные сети YOLO3, YOLO4 и YOLO5. Дообучение нейросетей YOLO3 и YOLO 4 осуществлялось на модифицированном датасете подводного мусора Trash-ICRA19, количество классов объектов в котором составляло 13, из которых 2 были фиктивными. При этом среднеклассовая точность детектирования 13 классов объектов с помощью YOLO4 по метрике mAP50 составляла 75.2% или, с учетом фиктивных классов, 88.873%. Для тестирования нейросетей были использованы изображения, полученные из видеозаписей процесса разминирования водоемов с помощью дистанционно управляемых подводных аппаратов (ROV).
Предложена усовершенствованная схема нейронной сети, представляющая собой каскад из нескольких последовательно соединенных YOLO-сегментов с многопроходной обработкой изображений. Разработаны рекомендации по дальнейшему повышению эффективности нейросетевого метода селекции подводных боеприпасов.
9
Zhou, Xinzhu, Guoxiang Sun, Naimin Xu, Xiaolei Zhang, Jiaqi Cai, Yunpeng Yuan, and Yinfeng Huang. "A Method of Modern Standardized Apple Orchard Flowering Monitoring Based on S-YOLO." Agriculture 13, no. 2 (February 4, 2023): 380. http://dx.doi.org/10.3390/agriculture13020380.
Abstract:
Monitoring fruit tree flowering information in the open world is more crucial than in the research-oriented environment for managing agricultural production to increase yield and quality. This work presents a transformer-based flowering period monitoring approach in an open world in order to better monitor the whole blooming time of modern standardized orchards utilizing IoT technologies. This study takes images of flowering apple trees captured at a distance in the open world as the research object, extends the dataset by introducing the Slicing Aided Hyper Inference (SAHI) algorithm, and establishes an S-YOLO apple flower detection model by substituting the YOLOX backbone network with Swin Transformer-tiny. The experimental results show that S-YOLO outperformed YOLOX-s in the detection accuracy of the four blooming states by 7.94%, 8.05%, 3.49%, and 6.96%. It also outperformed YOLOX-s by 10.00%, 9.10%, 13.10%, and 7.20% for mAPALL, mAPS, mAPM, and mAPL, respectively. By increasing the width and depth of the network model, the accuracy of the larger S-YOLO was 88.18%, 88.95%, 89.50%, and 91.95% for each flowering state and 39.00%, 32.10%, 50.60%, and 64.30% for each type of mAP, respectively. The results show that the transformer-based method of monitoring the apple flower growth stage utilized S-YOLO to achieve the apple flower count, percentage analysis, peak flowering time determination, and flowering intensity quantification. The method can be applied to remotely monitor flowering information and estimate flowering intensity in modern standard orchards based on IoT technology, which is important for developing fruit digital production management technology and equipment and guiding orchard production management.
10
Pan, Haixia, Jiahua Lan, Hongqiang Wang, Yanan Li, Meng Zhang, Mojie Ma, Dongdong Zhang, and Xiaoran Zhao. "UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection." Sensors 23, no. 10 (May 18, 2023): 4859. http://dx.doi.org/10.3390/s23104859.
Abstract:
Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models.
11
Wu, Yuhuan, and Yonghong Wu. "Enhanced YOLOX with United Attention Head for Road Detetion When Driving." Mathematics 12, no. 9 (April 27, 2024): 1331. http://dx.doi.org/10.3390/math12091331.
Abstract:
Object detection plays a crucial role in autonomous driving assistance systems. It requires high accuracy for prediction, a small size for deployment on mobile devices, and real-time inference speed to ensure safety. In this paper, we present a compact and efficient algorithm called YOLOX with United Attention Head (UAH-YOLOX) for detection in autonomous driving scenarios. By replacing the backbone network with GhostNet for feature extraction, the model reduces the number of parameters and computational complexity. By adding a united attention head before the YOLO head, the model effectively detects the scale, position, and contour features of targets. In particular, an attention module called Spatial Self-Attention is designed to extract spatial location information, demonstrating great potential in detection. In our network, the IOU Loss (Intersection of Union) has been replaced with CIOU Loss (Complete Intersection of Union). Further experiments demonstrate the effectiveness of our proposed methods on the BDD100k dataset and the Caltech Pedestrian dataset. UAH-YOLOX achieves state-of-the-art results by improving the detection accuracy of the BDD100k dataset by 1.70% and increasing processing speed by 3.37 frames per second (FPS). Visualization provides specific examples in various scenarios.
12
Ferdous, Md, and Sk Md Masudul Ahsan. "PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites." PeerJ Computer Science 8 (June 17, 2022): e999. http://dx.doi.org/10.7717/peerj-cs.999.
Abstract:
With numerous countermeasures, the number of deaths in the construction industry is still higher compared to other industries. Personal Protective Equipment (PPE) is constantly being improved to avoid these accidents, although workers intentionally or unintentionally forget to use such safety measures. It is challenging to manually run a safety check as the number of co-workers on a site can be large; however, it is a prime duty of the authority to provide maximum protection to the workers on the working site. From these motivations, we have created a computer vision (CV) based automatic PPE detection system that detects various types of PPE. This study also created a novel dataset named CHVG (four colored hardhats, vest, safety glass) containing eight different classes, including four colored hardhats, vest, safety glass, person body, and person head. The dataset contains 1,699 images and corresponding annotations of these eight classes. For the detection algorithm, this study has used the You Only Look Once (YOLO) family’s anchor-free architecture, YOLOX, which yields better performance than the other object detection models within a satisfactory time interval. Moreover, this study found that the YOLOX-m model yields the highest mean average precision (mAP) than the other three versions of the YOLOX.
13
Luan, Tian, Shixiong Zhou, Guokang Zhang, Zechun Song, Jiahui Wu, and Weijun Pan. "Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery." Sensors 24, no. 9 (April 24, 2024): 2710. http://dx.doi.org/10.3390/s24092710.
Abstract:
Target detection technology based on unmanned aerial vehicle (UAV)-derived aerial imagery has been widely applied in the field of forest fire patrol and rescue. However, due to the specificity of UAV platforms, there are still significant issues to be resolved such as severe omission, low detection accuracy, and poor early warning effectiveness. In light of these issues, this paper proposes an improved YOLOX network for the rapid detection of forest fires in images captured by UAVs. Firstly, to enhance the network’s feature-extraction capability in complex fire environments, a multi-level-feature-extraction structure, CSP-ML, is designed to improve the algorithm’s detection accuracy for small-target fire areas. Additionally, a CBAM attention mechanism is embedded in the neck network to reduce interference caused by background noise and irrelevant information. Secondly, an adaptive-feature-extraction module is introduced in the YOLOX network’s feature fusion part to prevent the loss of important feature information during the fusion process, thus enhancing the network’s feature-learning capability. Lastly, the CIoU loss function is used to replace the original loss function, to address issues such as excessive optimization of negative samples and poor gradient-descent direction, thereby strengthening the network’s effective recognition of positive samples. Experimental results show that the improved YOLOX network has better detection performance, with mAP@50 and mAP@50_95 increasing by 6.4% and 2.17%, respectively, compared to the traditional YOLOX network. In multi-target flame and small-target flame scenarios, the improved YOLO model achieved a mAP of 96.3%, outperforming deep learning algorithms such as FasterRCNN, SSD, and YOLOv5 by 33.5%, 7.7%, and 7%, respectively. It has a lower omission rate and higher detection accuracy, and it is capable of handling small-target detection tasks in complex fire environments. This can provide support for UAV patrol and rescue applications from a high-altitude perspective.
14
Zhang, Lijuan, Cuixing Zhao, Yuncong Feng, and Dongming Li. "Pests Identification of IP102 by YOLOv5 Embedded with the Novel Lightweight Module." Agronomy 13, no. 6 (June 12, 2023): 1583. http://dx.doi.org/10.3390/agronomy13061583.
Abstract:
The development of the agricultural economy is hindered by various pest-related problems. Most pest detection studies only focus on a single pest category, which is not suitable for practical application scenarios. This paper presents a deep learning algorithm based on YOLOv5, which aims to assist agricultural workers in efficiently diagnosing information related to 102 types of pests. To achieve this, we propose a new lightweight convolutional module called C3M, which is inspired by the MobileNetV3 network. Compared to the original convolution module C3, C3M occupies less computing memory and results in a faster inference speed, with the detection precision improved by 4.6%. In addition, the GAM (Global Attention Mechanism) is introduced into the neck of YOLO5, which further improves the detection capability of the model. The experimental results indicate that the C3M-YOLO algorithm performs better than YOLOv5 on IP102, a public dataset consisting of 102 pests. Specifically, the detection precision P is 2.4% higher than that of the original model, and mAP0.75 increased by 1.7%, while the F1-score improved by 1.8%. Furthermore, the mAP0.5 and mAP0.75 of the C3M-YOLO algorithm are higher than those of the YOLOX detection model by 5.1% and 6.2%, respectively.
15
Li, Yong, Ruichen Wang, Dongxu Gao, and Zhiyong Liu. "A Floating-Waste-Detection Method for Unmanned Surface Vehicle Based on Feature Fusion and Enhancement." Journal of Marine Science and Engineering 11, no. 12 (November 26, 2023): 2234. http://dx.doi.org/10.3390/jmse11122234.
Abstract:
Unmanned surface vehicle (USV)-based floating-waste detection presents significant challenges. Due to the water surface’s high reflectivity, there are often light spots and reflections in images captured by USVs. Furthermore, floating waste often consists of numerous small objects that prove difficult to detect, posing a robustness challenge for object-detection networks. To address these issues, we introduce a new dataset collected by USV, FloatingWaste-I, which accounts for the effects of light in various weather conditions, including sunny, cloudy, rainy and nighttime scenarios. This dataset comprises two types of waste: bottles and cartons. We also propose the innovative floating-waste-detection network, YOLO-Float, which incorporates a low-level representation-enhancement module and an attentional-fusion module. The former boosts the network’s low-level representation capability while the latter fuses the highest- and lowest-resolution feature map to improve the model robustness. We evaluated our method by using both the public dataset FloW-img and our FloatingWaste-I dataset. The results confirm YOLO-Float’s effectiveness, with an AP of 44.2% on the FloW-img dataset, surpassing the existing YOLOR, YOLOX and YOLOv7 by 3.2%, 2.7% and 3.4%, respectively.
16
Ma, Songzhe, Huimin Lu, Yifan Wang, and Han Xue. "YOLOX-Mobile: A Target Detection Algorithm More Suitable for Mobile Devices." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012030. http://dx.doi.org/10.1088/1742-6596/2203/1/012030.
Abstract:
Abstract With the continuous development of network, the increasing popularity of embedded technology, and the continuous improvement of mobile terminal computing ability, how to reduce the size of the model without affecting the accuracy as much as possible, and how to deploy the algorithm in embedded devices has become the current research hotspot. In this paper, we propose a new target detection algorithm called YOLOX-Mobile. Firstly, in order to further reduce the power consumption of embedded devices while maintaining the performance, we replaced the SiLU activation function in the original YOLOX with Mish activation function. Because SiLU activation functions occupy a certain amount of computing and storage resources, and the cost of calculating the Sigmoid type functions on mobile devices is much higher. Therefore, we use Mish functions that are smoother, non-monotonic, unbounded upper and lower bounds are used as activation functions to better meet the low power requirements of embedded devices. Secondly, we used Focal Loss as the Loss function of obj_output to achieve the balance of positive and negative samples, as well as hard-to-classify and easy-to-classify samples. Thirdly, we introduce the Involution operator and use it as the convolution kernel of 3×3. We validated the proposed algorithm on public dataset VOC2012 against the current mainstream YOLOX, YOLOX-M, and YOLOX-S algorithms. The mAP of our proposed algorithm is 78.22% and the detection speed is 55.26 FPS. Compared with the original YOlOX, YOLOX-M and YOLOX-S algorithms, the average FPS is improved by 1.99% and 4.13 FPS, and the average Params Size is reduced by 28.80%. Experimental results show that our proposed algorithm improves the accuracy and speed of detection on top of greatly reducing the network parameters and computation, making it a more suitable target detection algorithm for mobile devices.
17
Luo, Hui, Jiamin Li, Lianming Cai, and Mingquan Wu. "STrans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Pavement Crack Detection." Applied Sciences 13, no. 3 (February 3, 2023): 1999. http://dx.doi.org/10.3390/app13031999.
Abstract:
Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model the long-range dependencies between pixels and easily lose edge detail information in complex scenes. Moreover, irregular crack shapes also make the detection task challenging. To address these issues, an automatic pavement crack detection architecture named STrans-YOLOX is proposed. Specifically, the architecture first exploits the CNN backbone to extract feature information, preserving the local modeling ability of the CNN. Then, Swin Transformer is introduced to enhance the long-range dependencies through a self-attention mechanism by supplying each pixel with global features. A new global attention guidance module (GAGM) is used to ensure effective information propagation in the feature pyramid network (FPN) by using high-level semantic information to guide the low-level spatial information, thereby enhancing the multi-class and multi-scale features of cracks. During the post-processing stage, we utilize α-IoU-NMS to achieve the accurate suppression of the detection boxes in the case of occlusion and overlapping objects by introducing an adjustable power parameter. The experiments demonstrate that the proposed STrans-YOLOX achieves 63.37% mAP and surpasses the state-of-the-art models on the challenging pavement crack dataset.
18
Ren, Cheng, Shouming Hou, Jianchao Hou, and Yuteng Pang. "SwiF-YOLO: A Deep Learning Method for Lung Nodule Detection." International Journal of Biology and Life Sciences 5, no. 2 (March 29, 2024): 20–27. http://dx.doi.org/10.54097/rcx9h636.
Abstract:
Lung cancer, a prevalent and lethal tumor globally, has a five-year survival rate of only 10%-16% for late-stage patients. However, early diagnosis and treatment can increase this rate to 52%. Lung nodules, as crucial indicators of early lung cancer, are challenging to detect due to their small size and similar features to other lung tissues. Therefore, developing an automatic detection method to improve the efficiency and accuracy of lung nodule detection is vital. This paper proposes a new method based on the YOLOx model, called SwiF-YOLO, to enhance the precision and efficiency of lung nodule detection. We introduced the Swin transformer to replace the main network of yolox-m, adopted the Adaptively Spatial Feature Fusion (ASFF) as the feature fusion method, and replaced the Intersection over Union (IOU) regression loss function with Generalized Intersection over Union (GIoU). These improvements aim to enhance the accuracy and efficiency of lung nodule detection, assisting doctors in diagnosing more accurately and quickly.
19
Ma, Shihao, Jiao Wu, Zhijun Zhang, and Yala Tong. "Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images." Applied Sciences 14, no. 5 (March 5, 2024): 2158. http://dx.doi.org/10.3390/app14052158.
Abstract:
Addressing the limitations, including low automation, slow recognition speed, and limited universality, of current mudslide disaster detection techniques in remote sensing imagery, this study employs deep learning methods for enhanced mudslide disaster detection. This study evaluated six object detection models: YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOX, conducting experiments on remote sensing image data in the study area. Utilizing transfer learning, mudslide remote sensing images were fed into these six models under identical experimental conditions for training. The experimental results demonstrate that YOLOX-Nano’s comprehensive performance surpasses that of the other models. Consequently, this study introduces an enhanced model based on YOLOX-Nano (RS-YOLOX-Nano), aimed at further improving the model’s generalization capabilities and detection performance in remote sensing imagery. The enhanced model achieves a mean average precision (mAP) value of 86.04%, a 3.53% increase over the original model, and boasts a precision rate of 89.61%. Compared to the conventional YOLOX-Nano algorithm, the enhanced model demonstrates superior efficacy in detecting mudflow targets within remote sensing imagery.
20
Nguyen, Hoan-Viet, Jun-Hee Bae, Yong-Eun Lee, Han-Sung Lee, and Ki-Ryong Kwon. "Comparison of Pre-Trained YOLO Models on Steel Surface Defects Detector Based on Transfer Learning with GPU-Based Embedded Devices." Sensors 22, no. 24 (December 16, 2022): 9926. http://dx.doi.org/10.3390/s22249926.
Abstract:
Steel is one of the most basic ingredients, which plays an important role in the machinery industry. However, the steel surface defects heavily affect its quality. The demand for surface defect detectors draws much attention from researchers all over the world. However, there are still some drawbacks, e.g., the dataset is limited accessible or small-scale public, and related works focus on developing models but do not deeply take into account real-time applications. In this paper, we investigate the feasibility of applying stage-of-the-art deep learning methods based on YOLO models as real-time steel surface defect detectors. Particularly, we compare the performance of YOLOv5, YOLOX, and YOLOv7 while training them with a small-scale open-source NEU-DET dataset on GPU RTX 2080. From the experiment results, YOLOX-s achieves the best accuracy of 89.6% mAP on the NEU-DET dataset. Then, we deploy the weights of trained YOLO models on Nvidia devices to evaluate their real-time performance. Our experiments devices consist of Nvidia Jetson Nano and Jetson Xavier AGX. We also apply some real-time optimization techniques (i.e., exporting to TensorRT, lowering the precision to FP16 or INT8 and reducing the input image size to 320 × 320) to reduce detection speed (fps), thus also reducing the mAP accuracy.
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Lan, Yubin, Shaoming Lin, Hewen Du, Yaqi Guo, and Xiaoling Deng. "Real-Time UAV Patrol Technology in Orchard Based on the Swin-T YOLOX Lightweight Model." Remote Sensing 14, no. 22 (November 17, 2022): 5806. http://dx.doi.org/10.3390/rs14225806.
Abstract:
Using unmanned aerial vehicle (UAV) real-time remote sensing to monitor diseased plants or abnormal areas of orchards from a low altitude perspective can greatly improve the efficiency and response speed of the patrol in smart orchards. The purpose of this paper is to realize the intelligence of the UAV terminal and make the UAV patrol orchard in real-time. The existing lightweight object detection algorithms are usually difficult to consider both detection accuracy and processing speed. In this study, a new lightweight model named Swin-T YOLOX, which consists of the advanced detection network YOLOX and the strong backbone Swin Transformer, was proposed. Model layer pruning technology was adopted to prune the multi-layer stacked structure of the Swin Transformer. A variety of data enhancement strategies were conducted to expand the dataset in the model training stage. The lightweight Swin-T YOLOX model was deployed to the embedded platform Jetson Xavier NX to evaluate its detection capability and real-time performance of the UAV patrol mission in the orchard. The research results show that, with the help of TensorRT optimization, the proposed lightweight Swin-T YOLOX network achieved 94.0% accuracy and achieved a detection speed of 40 fps on the embedded platform (Jetson Xavier NX) for patrol orchard missions. Compared to the original YOLOX network, the model accuracy has increased by 1.9%. Compared to the original Swin-T YOLOX, the size of the proposed lightweight Swin-T YOLOX has been reduced to two-thirds, while the model accuracy has slightly increased by 0.7%. At the same time, the detection speed of the model has reached 40 fps, which can be applied to the real-time UAV patrol in the orchard.
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Lin, Junran, Cuimei Yang, Yi Lu, Yuxing Cai, Hanjie Zhan, and Zhen Zhang. "An Improved Soft-YOLOX for Garbage Quantity Identification." Mathematics 10, no. 15 (July 28, 2022): 2650. http://dx.doi.org/10.3390/math10152650.
Abstract:
Urban waterlogging is mainly caused by garbage clogging the sewer manhole covers. If the amount of garbage at a sewer manhole cover can be detected, together with an early warning signal when the amount is large enough, it will be of great significance in preventing urban waterlogging from occurring. Based on the YOLOX algorithm, this paper accomplishes identifying manhole covers and garbage and building a flood control system that can automatically recognize and monitor the accumulation of garbage. This system can also display the statistical results and send early warning information. During garbage identification, it can lead to inaccurate counting and a missed detection if the garbage is occluded. To reduce the occurrence of missed detections as much as possible and improve the performance of detection models, Soft-YOLOX, a method using a new detection model for counting, was used as it can prevent the occurrence of missed detections by reducing the scores of adjacent detection frames reasonably. The Soft-YOLOX improves the accuracy of garbage counting. Compared with the traditional YOLOX, the mAP value of Soft-YOLOX for garbage identification increased from 89.72% to 91.89%.
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Li, Zhiyong, Xueqin Jiang, Luyu Shuai, Boda Zhang, Yiyu Yang, and Jiong Mu. "A Real-Time Detection Algorithm for Sweet Cherry Fruit Maturity Based on YOLOX in the Natural Environment." Agronomy 12, no. 10 (October 12, 2022): 2482. http://dx.doi.org/10.3390/agronomy12102482.
Abstract:
Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model’s attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems.
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Guo, Yongping, Ying Chen, Jianzhi Deng, Shuiwang Li, and Hui Zhou. "Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark." Sensors 23, no. 1 (December 22, 2022): 92. http://dx.doi.org/10.3390/s23010092.
Abstract:
Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimation systems are based on RGB images, which do not seriously take into account personal privacy. Although identity-preserved algorithms are very desirable when human pose estimation is applied to scenarios where personal privacy does matter, developing human pose estimation algorithms based on identity-preserved modalities, such as thermal images concerned here, is very challenging due to the limited amount of training data currently available and the fact that infrared thermal images, unlike RGB images, lack rich texture cues which makes annotating training data itself impractical. In this paper, we formulate a new task with privacy protection that lies between human detection and human pose estimation by introducing a benchmark for IPHPDT (i.e., Identity-Preserved Human Posture Detection in Thermal images). This task has a threefold novel purpose: the first is to establish an identity-preserved task with thermal images; the second is to achieve more information other than the location of persons as provided by human detection for more advanced computer vision applications; the third is to avoid difficulties in collecting well-annotated data for human pose estimation in thermal images. The presented IPHPDT dataset contains four types of human postures, consisting of 75,000 images well-annotated with axis-aligned bounding boxes and postures of the persons. Based on this well-annotated IPHPDT dataset and three state-of-the-art algorithms, i.e., YOLOF (short for You Only Look One-level Feature), YOLOX (short for Exceeding YOLO Series in 2021) and TOOD (short for Task-aligned One-stage Object Detection), we establish three baseline detectors, called IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. In the experiments, three baseline detectors are used to recognize four infrared human postures, and the mean average precision can reach 70.4%. The results show that the three baseline detectors can effectively perform accurate posture detection on the IPHPDT dataset. By releasing IPHPDT, we expect to encourage more future studies into human posture detection in infrared thermal images and draw more attention to this challenging task.
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Liu, Sha, Yongliang Qiao, Jiawei Li, Haotian Zhang, Mingke Zhang, and Meili Wang. "An Improved Lightweight Network for Real-Time Detection of Apple Leaf Diseases in Natural Scenes." Agronomy 12, no. 10 (September 30, 2022): 2363. http://dx.doi.org/10.3390/agronomy12102363.
Abstract:
Achieving rapid and accurate detection of apple leaf diseases in the natural environment is essential for the growth of apple plants and the development of the apple industry. In recent years, deep learning has been widely studied and applied to apple leaf disease detection. However, existing networks have too many parameters to be easily deployed or lack research on leaf diseases in complex backgrounds to effectively use in real agricultural environments. This study proposes a novel deep learning network, YOLOX-ASSANano, which is an improved lightweight real-time model for apple leaf disease detection based on YOLOX-Nano. We improved the YOLOX-Nano backbone using a designed asymmetric ShuffleBlock, a CSP-SA module, and blueprint-separable convolution (BSConv), which significantly enhance feature-extraction capability and boost detection performance. In addition, we construct a multi-scene apple leaf disease dataset (MSALDD) for experiments. The experimental results show that the YOLOX-ASSANano model with only 0.83 MB parameters achieves 91.08% mAP on MSALDD and 58.85% mAP on the public dataset PlantDoc with a speed of 122 FPS. This study indicates that the YOLOX-ASSANano provides a feasible solution for the real-time diagnosis of apple leaf diseases in natural scenes, and could be helpful for the detection of other plant diseases.
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Yang, Lei, Guowu Yuan, Hao Zhou, Hongyu Liu, Jian Chen, and Hao Wu. "RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images." Applied Sciences 12, no. 17 (August 30, 2022): 8707. http://dx.doi.org/10.3390/app12178707.
Abstract:
Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS-YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive and negative samples in training, we used the Varifocal Loss function. Finally, to obtain a high-performance remote sensing object detector, we combined the trained model with an open-source framework called Slicing Aided Hyper Inference (SAHI). This work evaluated models on three aerial remote sensing datasets (DOTA-v1.5, TGRS-HRRSD, and RSOD). Our comparative experiments demonstrate that our model has the highest accuracy in detecting objects in remote sensing image datasets.
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Li, Shanni, Zhensheng Yang, Huabei Nie, and Xiao Chen. "Corn Disease Detection Based on an Improved YOLOX-Tiny Network Model." International Journal of Cognitive Informatics and Natural Intelligence 16, no. 1 (January 1, 2022): 1–8. http://dx.doi.org/10.4018/ijcini.309990.
Abstract:
In order to detect corn diseases accurately and quickly and reduce the impact of corn diseases on yield and quality, this paper proposes an improved object detection network named YOLOX-Tiny, which fuses convolutional attention module (CBAM), mixup data enhancement strategy, and center IOU loss function. The detection network uses the CSPNet network model as the backbone network and adds the CBAM to the feature pyramid network (FPN) of the structure, which re-assigns the feature maps' weight of different channels to enhance the extraction of deep information from the structure. The performance evaluation and comparison results of the methods show that the improved YOLOX-Tiny object detection network can effectively detect three common corn diseases, such as cercospora grayspot, northern blight, and commonrust. Compared with the traditional neural network models (90.89% of VGG-16, 97.32% of YOLOv4-tiny, 97.85% of YOLOX-Tiny, 97.91% of ResNet-50, and 97.31% of Faster RCNN), the presented improved YOLOX-Tiny network has higher accuracy.
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Hu, Chunsheng, Yong Zhao, Fangjuan Cheng, and Zhiping Li. "Multi-Object Detection Algorithm in Wind Turbine Nacelles Based on Improved YOLOX-Nano." Energies 16, no. 3 (January 18, 2023): 1082. http://dx.doi.org/10.3390/en16031082.
Abstract:
With more and more wind turbines coming into operation, inspecting wind farms has become a challenging task. Currently, the inspection robot has been applied to inspect some essential parts of the wind turbine nacelle. The detection of multiple objects in the wind turbine nacelle is a prerequisite for the condition monitoring of some essential parts of the nacelle by the inspection robot. In this paper, we improve the original YOLOX-Nano model base on the short monitoring time of the inspected object by the inspection robot and the slow inference speed of the original YOLOX-Nano. The accuracy and inference speed of the improved YOLOX-Nano model are enhanced, and especially, the inference speed of the model is improved by 72.8%, and it performs better than other lightweight network models on embedded devices. The improved YOLOX-Nano greatly satisfies the need for a high-precision, low-latency algorithm for multi-object detection in wind turbine nacelle.
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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.
Abstract:
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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Wu, You, Hengzhou Ye, Yaqing Yang, Zhaodong Wang, and Shuiwang Li. "Liquid Content Detection In Transparent Containers: A Benchmark." Sensors 23, no. 15 (July 25, 2023): 6656. http://dx.doi.org/10.3390/s23156656.
Abstract:
Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task.
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Ahmed, Faisal, Waheed Noor, Mohammad Atif Nasim, Ihsan Ullah, and Abdul Basit. "Vegetation and Non-Vegetation Classification Using Object Detection Techniques and Deep Learning from Low/Mixed Resolution Satellite Images." Pakistan Journal of Emerging Science and Technologies (PJEST) 4, no. 4 (December 30, 2023): 1–18. http://dx.doi.org/10.58619/pjest.v4i4.152.
Abstract:
Vegetation cover classification using mixed or low-resolution scalar images is challenging. Fortunately, recently deep learning object detection methods have emerged as a replacement to the conventional machine learning methods for the detection and classification of land use and land cover. This paper presents a deep learning object detection approach for land use and land cover detection using low/mixed resolution satellite images acquired from Google Earth satellite images. Google Earth images are accessible freely using the Google Earth Pro desktop application. Our dataset consists of two (02) classes (vegetation and non-vegetation) with a total of 450 labeled images captured from different parts of Pakistan. We present a comparison of the recent anchor-free object detection model YOLOX with the anchor-based object detection model YOLOR for solving real-time problems. The end-to-end differentiability, efficient GPU utilization, and absence of hand-crafted parameters make anchor-free models a compelling choice in object detection, and yet not been explored on Land cover classification using satellite images. Our experimental study shows that YOLOX delivers an overall accuracy of 83.50% on Vegetation and 86% on Non-Vegetation classes, which outperformed YOLOR by 30% on Vegetation classes and 34% on non-Vegetation classes for our dataset. We also show how an object detection system can be used for Vegetation and Non-Vegetation classification tasks, which can then be used for change monitoring and assisting in developing geographical maps using low/mixed resolution freely available satellite images.
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Hou, Heyi, Mingxia Chen, Yongbo Tie, and Weile Li. "A Universal Landslide Detection Method in Optical Remote Sensing Images Based on Improved YOLOX." Remote Sensing 14, no. 19 (October 3, 2022): 4939. http://dx.doi.org/10.3390/rs14194939.
Abstract:
Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX (You Only Look Once) target detection model to address the poor detection of complex mixed landslides. Wherein the VariFocal is used to replace the binary cross entropy in the original classification loss function to solve the uneven distribution of landslide samples and improve the detection recall; the coordinate attention (CA) mechanism is added to enhance the detection accuracy. Firstly, 1200 historical landslide optical remote sensing images in thirty-eight areas of China were extracted from Google Earth to create a mixed sample set for landslide detection. Next, the three attention mechanisms were compared to form the YOLOX-Pro model. Then, we tested the performance of YOLOX-Pro by comparing it with four models: YOLOX, YOLOv5, Faster R-CNN, and Single Shot MultiBox Detector (SSD). The results show that the YOLOX-Pro(m) has significantly improved the detection accuracy of complex and small landslides than the other models, with an average precision (AP0.75) of 51.5%, APsmall of 36.50%, and ARsmall of 49.50%. In addition, optical remote sensing images of a 12.32 km2 group-occurring landslides area located in Mibei village, Longchuan County, Guangdong, China, and 750 Unmanned Aerial Vehicle (UAV) images collected from the Internet were also used for landslide detection. The research results proved that the proposed method has strong generalization and good detection performance for many types of landslides, which provide a technical reference for the broad application of landslide detection using UAV.
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Ren, Keying, Xiaoyan Chen, Zichen Wang, Xiaoning Yan, and Dongyang Zhang. "Fruit Recognition Based on YOLOX*." Proceedings of International Conference on Artificial Life and Robotics 27 (January 20, 2022): 470–73. http://dx.doi.org/10.5954/icarob.2022.os11-3.
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Yao, Yuan, Guozhong Wang, and Jinhui Fan. "WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX." Energies 16, no. 9 (April 28, 2023): 3776. http://dx.doi.org/10.3390/en16093776.
Abstract:
Wind turbine blades will suffer various surface damages due to their operating environment and high-speed rotation. Accurate identification in the early stage of damage formation is crucial. The damage detection of wind turbine blades is a primarily manual operation, which has problems such as high cost, low efficiency, intense subjectivity, and high risk. The rise of deep learning provides a new method for detecting wind turbine blade damage. However, in detecting wind turbine blade damage in general network models, there will be an insufficient fusion of multiscale small target features. This paper proposes a lightweight cascaded feature fusion neural network model based on YOLOX. Firstly, the lightweight area of the backbone feature extraction network concerning the RepVGG network structure is enhanced, improving the model’s inference speed. Second, a cascaded feature fusion module is designed to cascade and interactively fuse multilevel features to enhance the small target area features and the model’s feature perception capabilities for multiscale target damage. The focal loss is introduced in the post-processing stage to enhance the network’s ability to learn complex positive sample damages. The detection accuracy of the improved algorithm is increased by 2.95%, the mAP can reach 94.29% in the self-made dataset, and the recall rate and detection speed are slightly improved. The experimental results show that the algorithm can autonomously learn the blade damage features from the wind turbine blade images collected in the actual scene, achieve the automatic detection, location, and classification of wind turbine blade damage, and promote the detection of wind turbine blade damage towards automation, rapidity, and low-cost development.
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Huang, Jiarun, Zhili He, Yuwei Guan, and Hongguo Zhang. "Real-Time Forest Fire Detection by Ensemble Lightweight YOLOX-L and Defogging Method." Sensors 23, no. 4 (February 8, 2023): 1894. http://dx.doi.org/10.3390/s23041894.
Abstract:
Forest fires can destroy forest and inflict great damage to the ecosystem. Fortunately, forest fire detection with video has achieved remarkable results in enabling timely and accurate fire warnings. However, the traditional forest fire detection method relies heavily on artificially designed features; CNN-based methods require a large number of parameters. In addition, forest fire detection is easily disturbed by fog. To solve these issues, a lightweight YOLOX-L and defogging algorithm-based forest fire detection method, GXLD, is proposed. GXLD uses the dark channel prior to defog the image to obtain a fog-free image. After the lightweight improvement of YOLOX-L by GhostNet, depth separable convolution, and SENet, we obtain the YOLOX-L-Light and use it to detect the forest fire in the fog-free image. To evaluate the performance of YOLOX-L-Light and GXLD, mean average precision (mAP) was used to evaluate the detection accuracy, and network parameters were used to evaluate the lightweight effect. Experiments on our forest fire dataset show that the number of the parameters of YOLOX-L-Light decreased by 92.6%, and the mAP increased by 1.96%. The mAP of GXLD is 87.47%, which is 2.46% higher than that of YOLOX-L; and the average fps of GXLD is 26.33 when the input image size is 1280 × 720. Even in a foggy environment, the GXLD can detect a forest fire in real time with a high accuracy, target confidence, and target integrity. This research proposes a lightweight forest fire detection method (GXLD) with fog removal. Therefore, GXLD can detect a forest fire with a high accuracy in real time. The proposed GXLD has the advantages of defogging, a high target confidence, and a high target integrity, which makes it more suitable for the development of a modern forest fire video detection system.
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Wu, Minghu, Leming Guo, Rui Chen, Wanyin Du, Juan Wang, Min Liu, Xiangbin Kong, and Jing Tang. "Improved YOLOX Foreign Object Detection Algorithm for Transmission Lines." Wireless Communications and Mobile Computing 2022 (October 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/5835693.
Abstract:
It is quite simple for foreign objects to attach themselves to transmission line corridors because of the wide variety of laying and the complex, changing environment. If these foreign objects are not found and removed in a timely manner, they can have a significant impact on the transmission lines’ ability to operate safely. Due to the problem of poor accuracy of foreign object identification in transmission line image inspection, we provide an improved YOLOX technique for detection of foreign objects in transmission lines. The method improves the YOLOX target detection network by first using Atrous Spatial Pyramid Pooling to increase sensitivity to foreign objects of different scales, then by embedding Convolutional Block Attention Module to increase model recognition accuracy, and finally by using GIoU loss to further optimize. The testing findings show that the enhanced YOLOX network has a mAP improvement of around 4.24% over the baseline YOLOX network. The target detection SSD, Faster R-CNN, YOLOv5, and YOLOV7 networks have improved less than this. The effectiveness and superiority of the algorithm are proven.
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Luo, Maolin, Linghua Xu, Yongliang Yang, Min Cao, and Jing Yang. "Laboratory Flame Smoke Detection Based on an Improved YOLOX Algorithm." Applied Sciences 12, no. 24 (December 15, 2022): 12876. http://dx.doi.org/10.3390/app122412876.
Abstract:
Fires in university laboratories often lead to serious casualties and property damage, and traditional sensor-based fire detection techniques suffer from fire warning delays. Current deep learning algorithms based on convolutional neural networks have the advantages of high accuracy, low cost, and high speeds in processing image-based data, but their ability to process the relationship between visual elements and objects is inferior to Transformer. Therefore, this paper proposes an improved YOLOX target detection algorithm combining Swin Transformer architecture, the CBAM attention mechanism, and a Slim Neck structure applied to flame smoke detection in laboratory fires. The experimental results verify that the improved YOLOX algorithm has higher detection accuracy and more accurate position recognition for flame smoke in complex situations, with APs of 92.78% and 92.46% for flame and smoke, respectively, and an mAP value of 92.26%, compared with the original YOLOX algorithm, SSD, Faster R-CNN, YOLOv4, and YOLOv5. The detection accuracy is improved, which proves the effectiveness and superiority of this improved YOLOX target detection algorithm in fire detection.
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Xu, Ying, Dongsheng Zhong, Jianhong Zhou, Ziyi Jiang, Yikui Zhai, and Zilu Ying. "A Novel UAV Visual Positioning Algorithm Based on A-YOLOX." Drones 6, no. 11 (November 18, 2022): 362. http://dx.doi.org/10.3390/drones6110362.
Abstract:
The application of UAVs is becoming increasingly extensive. However, high-precision autonomous landing is still a major industry difficulty. The current algorithm is not well-adapted to light changes, scale transformations, complex backgrounds, etc. To address the above difficulties, a deep learning method was here introduced into target detection and an attention mechanism was incorporated into YOLOX; thus, a UAV positioning algorithm called attention-based YOLOX (A-YOLOX) is proposed. Firstly, a novel visual positioning pattern was designed to facilitate the algorithm’s use for detection and localization; then, a UAV visual positioning database (UAV-VPD) was built through actual data collection and data augmentation and the A-YOLOX model detector developed; finally, corresponding high- and low-altitude visual positioning algorithms were designed for high- and low-altitude positioning logics. The experimental results in the actual environment showed that the AP50 of the proposed algorithm could reach 95.5%, the detection speed was 53.7 frames per second, and the actual landing error was within 5 cm, which meets the practical application requirements for automatic UAV landing.
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Hsieh, Chen-Chiung, Men-Ru Lu, and Hsiao-Ting Tseng. "Automatic Speaker Positioning in Meetings Based on YOLO and TDOA." Sensors 23, no. 14 (July 8, 2023): 6250. http://dx.doi.org/10.3390/s23146250.
Abstract:
In recent years, many things have been held via video conferences due to the impact of the COVID-19 epidemic around the world. A webcam will be used in conjunction with a computer and the Internet. However, the network camera cannot automatically turn and cannot lock the screen to the speaker. Therefore, this study uses the objection detector YOLO to capture the upper body of all people on the screen and judge whether each person opens or closes their mouth. At the same time, the Time Difference of Arrival (TDOA) is used to detect the angle of the sound source. Finally, the person’s position obtained by YOLO is reversed to the person’s position in the spatial coordinates through the distance between the person and the camera. Then, the spatial coordinates are used to calculate the angle between the person and the camera through inverse trigonometric functions. Finally, the angle obtained by the camera, and the angle of the sound source obtained by the microphone array, are matched for positioning. The experimental results show that the recall rate of positioning through YOLOX-Tiny reached 85.2%, and the recall rate of TDOA alone reached 88%. Integrating YOLOX-Tiny and TDOA for positioning, the recall rate reached 86.7%, the precision rate reached 100%, and the accuracy reached 94.5%. Therefore, the method proposed in this study can locate the speaker, and it has a better effect than using only one source.
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Changhao Dong, Changhao Dong, Chao Zhang Changhao Dong, Jianjun Li Chao Zhang, and Jiaxue Liu Jianjun Li. "Surface Defect Recognition of Wind Turbine Blades Based on Improved YOLOX-X Model." 電腦學刊 34, no. 2 (April 2023): 019–27. http://dx.doi.org/10.53106/199115992023043402002.
Abstract:
<p>In order to solve the problem of small data sets and small detected targets in image detection of wind turbine blades. In this paper, we propose an improved YOLOX-X model. Firstly, we use a variety of data set enhancement methods to solve the problem of small data sets. Secondly, an improved Mixup image enhancement method is proposed to enrich the image background. Then, the attention mechanisms of ECAnet and CBAM are introduced to improve the attention of important features. Furthermore, the IOU_LOSS loss function in the original model is replaced with CIOU_LOSS in this paper to improve the positioning accuracy of small target. Last but not least, the overall network uses the Adam optimizer to accelerate network training and recognition. The effectiveness of algorithm is evaluated on a data sets captured by a UAV in a wind farm. Compared with the original YOLOX-X model, our algorithm improves mAP by 4.55%. In addition, compared with other types of YOLO series networks, it is proved that our model is superior to other algorithms. </p> <p> </p>
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Wang, Yuxue, Hao Dong, Songyu Bai, Yang Yu, and Qingwei Duan. "Image Recognition and Classification of Farmland Pests Based on Improved Yolox-tiny Algorithm." Applied Sciences 14, no. 13 (June 26, 2024): 5568. http://dx.doi.org/10.3390/app14135568.
Abstract:
In order to rapidly detect pest types in farmland and mitigate their adverse effects on agricultural production, we proposed an improved Yolox-tiny-based target detection method for farmland pests. This method enhances the detection accuracy of farmland pests by limiting downsampling and incorporating the Convolution Block Attention Module (CBAM). In the experiments, images of pests common to seven types of farmland and particularly harmful to crops were processed through the original Yolox-tiny model after preprocessing and partial target expansion for comparative training and testing. The results indicate that the improved Yolox-tiny model increased the average precision by 7.18%, from 63.55% to 70.73%, demonstrating enhanced precision in detecting farmland pest targets compared to the original model.
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Tu, Shuqin, Qiantao Zeng, Yun Liang, Xiaolong Liu, Lei Huang, Shitong Weng, and Qiong Huang. "Automated Behavior Recognition and Tracking of Group-Housed Pigs with an Improved DeepSORT Method." Agriculture 12, no. 11 (November 12, 2022): 1907. http://dx.doi.org/10.3390/agriculture12111907.
Abstract:
Pig behavior recognition and tracking in group-housed livestock are effective aids for health and welfare monitoring in commercial settings. However, due to demanding farm conditions, the targets in the pig videos are heavily occluded and overlapped, and there are illumination changes, which cause error switches of pig identify (ID) in the tracking process and decrease the tracking quality. To solve these problems, this study proposed an improved DeepSORT algorithm for object tracking, which contained three processes. Firstly, two detectors, YOLOX-S and YOLO v5s, were developed to detect pig targets and classify four types of pig behaviors including lying, eating, standing, and other. Then, the improved DeepSORT was developed for pig behavior tracking and reducing error changes of pig ID by improving trajectory processing and data association. Finally, we established the public dataset annotation of group-housed pigs, with 3600 images in a total from 12 videos, which were suitable for pig tracking applications. The advantage of our method includes two aspects. One is that the trajectory processing and data association are improved by aiming at pig-specific scenarios, which are indoor scenes, and the number of pig target objects is stable. This improvement reduces the error switches of pig ID and enhances the stability of the tracking. The other is that the behavior classification information from the detectors is introduced into the tracking algorithm for behavior tracking. In the experiments of pig detection and behavior recognition, the YOLO v5s and YOLOX-S detectors achieved a high precision rate of 99.4% and 98.43%, a recall rate of 99% and 99.23, and a mean average precision (mAP) rate of 99.50% and 99.23%, respectively, with an AP.5:.95 of 89.3% and 87%. In the experiments of pig behavior tracking, the improved DeepSORT algorithm based on YOLOX-S obtained multi-object tracking accuracy (MOTA), ID switches (IDs), and IDF1 of 98.6%,15, and 95.7%, respectively. Compared with DeepSORT, it improved by 1.8% and 6.8% in MOTA and IDF1, respectively, and IDs had a significant decrease, with a decline of 80%. These experiments demonstrate that the improved DeepSORT can achieve pig behavior tracking with stable ID values under commercial conditions and provide scalable technical support for contactless automated pig monitoring.
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Li, Huaicheng, Hengwei Zhang, Yisheng Zhang, Shengmin Zhang, Yanlong Peng, Zhigang Wang, Huawei Song, and Ming Chen. "An Accurate Activate Screw Detection Method for Automatic Electric Vehicle Battery Disassembly." Batteries 9, no. 3 (March 21, 2023): 187. http://dx.doi.org/10.3390/batteries9030187.
Abstract:
With the increasing popularity of electric vehicles, the number of end-of-life (EOF) electric vehicle batteries (EVBs) is also increasing day by day. Efficient dismantling and recycling of EVBs are essential to ensure environmental protection. There are many types of EVBs with complex structures, and the current automatic dismantling line is immature and lacks corresponding dismantling equipment. This makes it difficult for some small parts to be disassembled precisely. Screws are used extensively in batteries to fix or connect modules in EVBs. However, due to the small size of screws and differences in installation angles, screw detection is a very challenging task and a significant obstacle to automatic EVBs disassembly. This research proposes a systematic method to complete screw detection called “Active Screw Detection”. The experimental results show that with the YOLOX-s model, the improved YOLOX model achieves 95.92% and 92.14% accuracy for both mAP50 and mAP75 positioning after autonomous adjustment of the robotic arm attitude. Compared to the method without autonomous adjustment of the robotic arm, mAP50 and mAP75 improved by 62.81% and 57.67%, respectively. In addition, the improved YOLOX model improves mAP50 and mAP75 by 0.19% and 3.59%, respectively, compared to the original YOLOX model.
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Wang, Biwen, Jing Zhou, Martin Costa, Shawn M. Kaeppler, and Zhou Zhang. "Plot-Level Maize Early Stage Stand Counting and Spacing Detection Using Advanced Deep Learning Algorithms Based on UAV Imagery." Agronomy 13, no. 7 (June 27, 2023): 1728. http://dx.doi.org/10.3390/agronomy13071728.
Abstract:
Phenotyping is one of the most important processes in modern breeding, especially for maize, which is an important crop for food, feeds, and industrial uses. Breeders invest considerable time in identifying genotypes with high productivity and stress tolerance. Plant spacing plays a critical role in determining the yield of crops in production settings to provide useful management information. In this study, we propose an automated solution using unmanned aerial vehicle (UAV) imagery and deep learning algorithms to provide accurate stand counting and plant-level spacing variabilities (PSV) in order to facilitate the breeders’ decision making. A high-resolution UAV was used to train three deep learning models, namely, YOLOv5, YOLOX, and YOLOR, for both maize stand counting and PSV detection. The results indicate that after optimizing the non-maximum suppression (NMS) intersection of union (IoU) threshold, YOLOv5 obtained the best stand counting accuracy, with a coefficient of determination (R2) of 0.936 and mean absolute error (MAE) of 1.958. Furthermore, the YOLOX model subsequently achieved an F1-score value of 0.896 for PSV detection. This study shows the promising accuracy and reliability of processed UAV imagery for automating stand counting and spacing evaluation and its potential to be implemented further into real-time breeding decision making.
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Liu, Youfu, Deqin Xiao, Jiaxin Zhou, and Shengqiu Zhao. "AFF-YOLOX: An improved lightweight YOLOX network to detect early hatching information of duck eggs." Computers and Electronics in Agriculture 210 (July 2023): 107893. http://dx.doi.org/10.1016/j.compag.2023.107893.
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Feng, Jintao, Zhipeng Wang, Shuai Wang, Shijie Tian, and Huirong Xu. "MSDD-YOLOX: An enhanced YOLOX for real-time surface defect detection of oranges by type." European Journal of Agronomy 149 (September 2023): 126918. http://dx.doi.org/10.1016/j.eja.2023.126918.
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Zhang, Jianfei, and Sai Ke. "Improved YOLOX Fire Scenario Detection Method." Wireless Communications and Mobile Computing 2022 (March 10, 2022): 1–8. http://dx.doi.org/10.1155/2022/9666265.
Abstract:
Considering the problems of existing target detection model difficulty for use in complicated fire scenarios and few detection targets, an improved YOLOX fire scenario detection model was introduced, to realize multitarget detection of flame, smoke, and persons: firstly, a light attention module, for improving the overall detection performance of the model; secondly, the channel shuffle technique was employed, for increasing the communication ability between channels; and finally, the backbone channel was replaced with a light transformer module, for enhancing the capture ability of the backbone channel for global information. As shown in the experiment with self-developed fire dataset, mAP of T-YOLOX increased by 2.24% as compared with the benchmark model (YOLOX), and the detection accuracy was significantly improved as compared with that of CenterNet and YOLOv3, showing the effectiveness and advantages of the algorithm.
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Liao, Weichen. "Research on Detection Algorithm of Safety helmet based on improved YOLOX." Frontiers in Computing and Intelligent Systems 3, no. 2 (April 18, 2023): 136–38. http://dx.doi.org/10.54097/fcis.v3i2.7690.
Abstract:
In the industrial production, construction and other fields, safety helmet as an important safety protective equipment, the wearing situation of workers personal safety and property safety is of great significance. Therefore, this paper proposes a safety helmet detection method based on the improved YOLOX algorithm. First, the 5000 pictures of safety helmet wearing at construction sites are labelled. The Squeeze and-Excitation module is introduced in the YOLOX network structure. The original Loss function is replaced with varifocal Loss. After experimental verification, compared with the original YOLOX target detection algorithm, our algorithm improves by 2.13 percentage points, enhances the model's focus on key areas and optimizes the model training effect, while the number of model parameters does not increase significantly. In conclusion, our algorithm has a wide range of application prospects and research value.
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Wang, Jun, Shuman Qi, Chao Wang, Jin Luo, Xin Wen, and Rui Cao. "B-YOLOX-S: A Lightweight Method for Underwater Object Detection Based on Data Augmentation and Multiscale Feature Fusion." Journal of Marine Science and Engineering 10, no. 11 (November 16, 2022): 1764. http://dx.doi.org/10.3390/jmse10111764.
Abstract:
With the increasing maturity of underwater agents-related technologies, underwater object recognition algorithms based on underwater robots have become a current hotspot for academic and applied research. However, the existing underwater imaging conditions are poor, the images are blurry, and the underwater robot visual jitter and other factors lead to lower recognition precision and inaccurate positioning in underwater target detection. A YOLOX-based underwater object detection model, B-YOLOX-S, is proposed to detect marine organisms such as echinus, holothurians, starfish, and scallops. First, Poisson fusion is used for data amplification at the input to balance the number of detected targets. Then, wavelet transform is used to perform Style Transfer on the enhanced images to achieve image restoration. The clarity of the images and detection targets is further increased and the generalization of the model is enhanced. Second, a combination of BIFPN-S and FPN is proposed to fuse the effective feature layer obtained by the Backbone layer to enhance the detection precision and accelerate model detection. Finally, the localization loss function of the prediction layer in the network is replaced by EIoU_Loss to heighten the localization precision in detection. Experimental results comparing the B-YOLOX-S algorithm model with mainstream algorithms such as FasterRCNN, YOLOV3, YOLOV4, YOLOV5, and YOLOX on the URPC2020 dataset show that the detection precision and detection speed of the algorithm model have obvious advantages over other algorithm networks. The average detection accuracy mAP value is 82.69%, which is 5.05% higher than the benchmark model (YOLOX-s), and the recall rate is 8.03% higher. Thus, the validity of the algorithmic model proposed in this paper is demonstrated.
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Yang, Lei, Guowu Yuan, Hao Wu, and Wenhua Qian. "An ultra-lightweight detector with high accuracy and speed for aerial images." Mathematical Biosciences and Engineering 20, no. 8 (2023): 13947–73. http://dx.doi.org/10.3934/mbe.2023621.
Abstract:
<abstract> <p>Aerial remote sensing images have complex backgrounds and numerous small targets compared to natural images, so detecting targets in aerial images is more difficult. Resource exploration and urban construction planning need to detect targets quickly and accurately in aerial images. High accuracy is undoubtedly the advantage for detection models in target detection. However, high accuracy often means more complex models with larger computational and parametric quantities. Lightweight models are fast to detect, but detection accuracy is much lower than conventional models. It is challenging to balance the accuracy and speed of the model in remote sensing image detection. In this paper, we proposed a new YOLO model. We incorporated the structures of YOLOX-Nano and slim-neck, then used the SPPF module and SIoU function. In addition, we designed a new upsampling paradigm that combined linear interpolation and attention mechanism, which can effectively improve the model's accuracy. Compared with the original YOLOX-Nano, our model had better accuracy and speed balance while maintaining the model's lightweight. The experimental results showed that our model achieved high accuracy and speed on NWPU VHR-10, RSOD, TGRS-HRRSD and DOTA datasets.</p> </abstract>