Journal articles on the topic 'Deep Learning, Computer Vision, Object Detection'
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Poojitha, L. "Anomalous Object Detection with Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3227–32. http://dx.doi.org/10.22214/ijraset.2022.44581.
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Abstract: In many computers vision systems, object identification and monitoring are crucial characteristics. Object identification and monitoring is a difficult job in the fields of computer vision that attempts to identify, recognize and track things across a video series of pictures. It aids in the understanding and description of object behaviour rather than relying on human operators to monitor the computers. Its goal is to find moving things in a video clip or a security camera. On the other hand, rely heavily on computers, on the other hand many respect attributes approaches. The system collects a snapshot from the camera, process it as per the models requirements and the outputs the data to the tensorflow framework which provides a list of the frames discoveries and the objects dependability points and connections to their binding containers. Before exposing the object to the user, the software takes those connections and creates a rectangle adjacent to it. This research detects the presence of anomalous items in camera-captured sequences, with anomalies being things that correspond to categories that should not be present in a given scene.
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Singh, Baljeet, Nitin Kumar, Irshad Ahmed, and Karun Yadav. "Real-Time Object Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3159–60. http://dx.doi.org/10.22214/ijraset.2022.42820.
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Abstract: The computer vision field known as real-time acquisition is large, dynamic, and complex. Local image process refers to the acquisition of one object in an image, while Objects refers to the acquisition of multiple objects in an image. In digital photos and videos, this sees semantic class objects. Tracking features, video surveilance, pedestrian detection, census, self-driving cars, face recognition, sports tracking, and many other applications used to find real-time object. Convolution Neural Networks is an in-depth study tool for OpenCV (Opensource Computer Vision), a set of basic computer-assisted programming tasks. Computer visualization, in-depth study, and convolutional neural networks are some of the words used in this paper..
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Pernando, Yonky, Eka Lia Febrianti, Ilwan Syafrinal, Yuni Roza, and Ummul Fitri Afifah. "DEEP LEARNING FOR FACES ON ORPHANAGE CHILDREN FACE DETECTION." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 9, no. 1 (December 16, 2022): 25–32. http://dx.doi.org/10.33330/jurteksi.v9i1.1858.
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Abstract: l -The field of computer vision is research in development technology to obtain information from images and replicate or imitate human visual processes, so that they can understand the objects around them. Deep learning is a term used to describe a new era in learning that supports computer learning from big data machines. Convolutional Neural Networks (CNN) algorithms have made significant progress in the fields of object detection, image classification, and semantic segmentation. ;Object detection is a technique used to identify the type of object in a given image and the location of the object in the image. The field of computer vision is research in development technology to obtain information from images and replicate or imitate human visual processes, so that computers can know objects around them. Deep learning is the buzzword as a new era in machine learning that trains computers to find patterns from large amounts of data. Convolutional Neural Networks (CNN) algorithms have made significant progress in the fields of object detection, image classification, and semantic segmentation. Object detection is a technique used to identify the type of object in a particular image as well as the location of the object in the image. Keywords: CNN, Computer Vision, Deep Learning, Face Detection; Abstrak: 1 Bidang computer vision merupakan penelitian dalam teknologi pembangunan untuk memperoleh informasi dari citra dan mereplikasi atau meniru proses visual manusia, sehingga dapat memahami objek - objek disekelilingnya. Pembelajaran mendalam adalah istilah yang digunakan untuk menggambarkan era baru dalam pembelajaran mesin yang memungkinkan komputer belajar dari sejumlah besar data. [Algoritma Convolutional Neural Networks (CNN) telah membuat kemajuan yang signifikan di bidang deteksi objek, klasifikasi gambar, dan segmentasi semantik. Deteksi objek adalah teknik yang digunakan untuk mengidentifikasi jenis objek dalam citra yang diberikan serta lokasi objek di dalam citra. Bidang computer vision merupakan penelitian dalam teknologi pembangunan untuk memperoleh informasi dari citra dan mereplikasi atau meniru proses visual manusia, sehingga komputer dapat mengetahui objek - objek disekelilingnya. Deep learning adalah kata kunci sebagai era baru dalam machine learning yang melatih komputer dalam menemukan pola dari jumlah besar data. Algoritma Convolutional Neural Networks (CNN) telah membuat kemajuan yang signifikan di bidang deteksi objek, klasifikasi gambar, dan segmentasi semantik. /Deteksi objek adalah teknik yang digunakan untuk mengidentifikasi jenis objek dalam citra tertentu serta lokasi objek di dalam citra. Kata kunci: CNN, Computer Vision, Deep Learning, Deteksi Wajah
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Singh, Ankita. "Face Mask Detection using Deep Learning to Manage Pandemic Guidelines." Journal of Management and Service Science (JMSS) 1, no. 2 (2021): 1–21. http://dx.doi.org/10.54060/jmss/001.02.003.
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The field of Computer Vision is a branch of science of the computers and systems of software in which one can visualize and as well as comprehend the images and scenes given in the input. This field is consisting of numerous aspects for example image recognition, the detection of objects, generation of images, image super resolution and more others. Object detection is broadly utilized for the detection of faces, the detection of vehicles, counting of pedestrians on a certain street, images displayed on the web, security systems and cars with the feature of self-driving. This process also encompasses the precision of every technique for recognizing the objects. The detection of objects is a crucial task; however, it is also a very challenging vision task. It is an analytical subdivide of various applications such as searching of images, image auto-annotation or scene understanding and tracking of various objects. The tracking of objects in motion of video image sequence was one of the most important subjects in computer vision.
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Zhu, Juncai, Zhizhong Wang, Songwei Wang, and Shuli Chen. "Moving Object Detection Based on Background Compensation and Deep Learning." Symmetry 12, no. 12 (November 27, 2020): 1965. http://dx.doi.org/10.3390/sym12121965.
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Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.
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Taralathasri, Bobburi, Dammati Vidya Sri, Gadidammalla Narendra Kumar, Annam Subbarao, and Palli R. Krishna Prasad. "REAL TIME OBJECT DETECTION USING YOLO ALGORITHM." International Journal of Computer Science and Mobile Computing 10, no. 7 (July 30, 2021): 61–67. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.009.
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The major and wide range applications like Driverless cars, robots, Image surveillance has become famous in the Computer vision .Computer vision is the core in all those applications which is responsible for the image detection and it became more popular worldwide. Object Detection System using Deep Learning Technique” detects objects efficiently based on YOLO algorithm and applies the algorithm on image data to detect objects.
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Jyothi, Madapati Asha, and Mr M. Kalidas. "Real Time Smart Object Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 212–17. http://dx.doi.org/10.22214/ijraset.2022.47281.
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Abstract: Efficient and accurate object detection has been an important topic in the advancement of computer vision systems. With the advent of deep learning techniques, the accuracy for object detection has increased drastically. The project aims to incorporate state-of-the-art technique for object detection with the goal of achieving high accuracy with a real-time performance. A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the deep learning based approach, which leads to slow and non-optimal performance. In this project, we use a completely deep learning based approach to solve the problem of object detection in an end-to-end fashion. The network is trained on the most challenging publicly available data-set, on which a object detection challenge is conducted annually. The resulting system is fast and accurate, thus aiding those applications which require object detection
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Kumar, Aayush, Amit Kumar, Avanish Chandra, and Indira Adak. "Custom Object Detection and Analysis in Real Time: YOLOv4." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3982–90. http://dx.doi.org/10.22214/ijraset.2022.43303.
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Abstract: Object recognition is one of the most basic and complex problems in computer vision, which seeks to locate object instances from the enormous categories of already defined and readily available natural images. The object detection method aims to recognize all the objects or entities in the given picture and determine the categories and position information to achieve machine vision understanding. Several tactics have been put forward to solve this problem, which is more or less inspired by the principles based on Open Source Computer Vision Library (OpenCV) and Deep Learning. Some are relatively good, while others fail to detect objects with random geometric transformations. This paper proposes demonstrating the " HAWKEYE " application, a small initiative to build an application working on the principle of EEE i.e. (Explore→Experience→Evolve). Keywords: Convolution Neural Network, Object detection, Image classification, Deep learning, Open CV, Yolov4.
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Saiful, Muhammad, Lalu Muhammad Samsu, and Fathurrahman Fathurrahman. "Sistem Deteksi Infeksi COVID-19 Pada Hasil X-Ray Rontgen menggunakan Algoritma Convolutional Neural Network (CNN)." Infotek : Jurnal Informatika dan Teknologi 4, no. 2 (July 31, 2021): 217–27. http://dx.doi.org/10.29408/jit.v4i2.3582.
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The development of the world's technology is growing rapidly, especially in the field of health in the form of detection tools of various objects, including disease objects. The technology in point is part of artificial intelligence that is able to recognize a set of imagery and classify automatically with deep learning techniques. One of the deep learning networks widely used is convolutional neural network with computer vision technology. One of the problems with computer vision that is still developing is object detection as a useful technology to recognize objects in the image as if humans knew the object of the image. In this case, a computer machine is trained in learning using artificial neural networks. One of the sub types of artificial neural networks that are able to handle computer vision problems is by using deep learning techniques with convolutional neural network algorithms. The purpose of this research is to find out how to design the system, the network architecture used for COVID-19 infection detection. The system cannot perform detection of other objects. The results of COVID-19 infection detection with convolutional neural network algorithm show unlimited accuracy value that ranges from 60-99%.
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Kumar, Chandan. "Hill Climb Game Play with Webcam Using OpenCV." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (January 31, 2022): 441–53. http://dx.doi.org/10.22214/ijraset.2022.39860.
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Abstract: Computer vision is a process by which we can understand how the images and videos are stored and manipulated, also it helps in the process of retrieving data from either images or videos. Computer Vision is part of Artificial Intelligence. Computer-Vision plays a major role in Autonomous cars, Object detections, robotics, object tracking, etc. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. It comes with a highly improved deep learning (dnn ) module. This module now supports a number of deep learning frameworks, including Caffe, TensorFlow, and Torch/PyTorch. This does allow us to take our models trained using dedicated deep learning libraries/tools and then efficiently use them directly inside our OpenCV scripts. MediaPipe is a framework mainly used for building audio, video, or any time series data. With the help of the MediaPipe framework, we can build very impressive pipelines for different media processing functions like Multi-hand Tracking, Face Detection, Object Detection and Tracking, etc.
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Matsuzaka, Yasunari, and Ryu Yashiro. "AI-Based Computer Vision Techniques and Expert Systems." AI 4, no. 1 (February 23, 2023): 289–302. http://dx.doi.org/10.3390/ai4010013.
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Computer vision is a branch of computer science that studies how computers can ‘see’. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise ‘vision’ among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts’ brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to ‘acquire the tacit knowledge of experts’, which was not previously achievable with conventional expert systems. Machine learning ‘systematises tacit knowledge’ based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning.
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Xin, Sun. "Application of Deep learning in computer vision." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 125–30. http://dx.doi.org/10.54097/hset.v16i.2494.
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The application of artificial intelligence is deep learning which is one of the current topics in the computer field as well as for the application of computer vision. With the continuous enhancement of deep learning, the algorithm performance is constantly updated. This review paper provides a brief overview of the basic concepts of computer vision and deep learning. Image classification, semantic segmentation and object detection are introduced in this paper followed by a description of their real world applications in various computer vision tasks, such as smart transportation and face recognition. Afterwards, the main applications of deep learning in the research field are demonstrated in this paper.
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Wang, Dadong, Jian-Gang Wang, and Ke Xu. "Deep Learning for Object Detection, Classification and Tracking in Industry Applications." Sensors 21, no. 21 (November 5, 2021): 7349. http://dx.doi.org/10.3390/s21217349.
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Kolluri, Johnson, and Ranjita Das. "An Evaluation of Deep Learning-Based Object Identification." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (December 9, 2022): 52–80. http://dx.doi.org/10.17762/ijritcc.v10i1s.5795.
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Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study.
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Fu, Yanzhe. "Recent Deep Learning Approaches for Object Detection." Highlights in Science, Engineering and Technology 31 (February 10, 2023): 64–70. http://dx.doi.org/10.54097/hset.v31i.4814.
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Object detection, a classic problem in computer vision, has been developed for more than 20 years. From the early traditional methods to today's deep learning methods, the accuracy is getting higher and higher, and the speed is getting faster and faster, which is benefit from deep learning and the continuous development of deep neural networks. Although the research on object detection is constantly developing, there are not many reviews on object detection, so this article will review the object detection after the introduction of deep learning. This article will first introduce the history of object detection, and then focus on a systematic introduction to the development of deep learning object detection in recent years, as well as one-stage detector and two-stage detector in anchor-free and anchor-based. The various methods applied in the stage detector will be sorted out, and the potential problems and future development of object detection will also be analyzed.
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Gupta, Ashish Kumar, Ayan Seal, Mukesh Prasad, and Pritee Khanna. "Salient Object Detection Techniques in Computer Vision—A Survey." Entropy 22, no. 10 (October 19, 2020): 1174. http://dx.doi.org/10.3390/e22101174.
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Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.
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Nguyen, Nhat-Duy, Tien Do, Thanh Duc Ngo, and Duy-Dinh Le. "An Evaluation of Deep Learning Methods for Small Object Detection." Journal of Electrical and Computer Engineering 2020 (April 27, 2020): 1–18. http://dx.doi.org/10.1155/2020/3189691.
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Small object detection is an interesting topic in computer vision. With the rapid development in deep learning, it has drawn attention of several researchers with innovations in approaches to join a race. These innovations proposed comprise region proposals, divided grid cell, multiscale feature maps, and new loss function. As a result, performance of object detection has recently had significant improvements. However, most of the state-of-the-art detectors, both in one-stage and two-stage approaches, have struggled with detecting small objects. In this study, we evaluate current state-of-the-art models based on deep learning in both approaches such as Fast RCNN, Faster RCNN, RetinaNet, and YOLOv3. We provide a profound assessment of the advantages and limitations of models. Specifically, we run models with different backbones on different datasets with multiscale objects to find out what types of objects are suitable for each model along with backbones. Extensive empirical evaluation was conducted on 2 standard datasets, namely, a small object dataset and a filtered dataset from PASCAL VOC 2007. Finally, comparative results and analyses are then presented.
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Gururaj, Vaishnavi, Shriya Varada Ramesh, Sanjana Satheesh, Ashwini Kodipalli, and Kusuma Thimmaraju. "Analysis of deep learning frameworks for object detection in motion." International Journal of Knowledge-based and Intelligent Engineering Systems 26, no. 1 (June 8, 2022): 7–16. http://dx.doi.org/10.3233/kes-220002.
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Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compared to VGG16, VGG19 and Inception V3 and is therefore chosen to be used with SSD. The impact of the differences in the amount of training of each algorithm is highlighted which helps understand the advantages and disadvantages of each algorithm and deduce the most suitable.
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Nguyen, Huu Thu, Eon-Ho Lee, Chul Hee Bae, and Sejin Lee. "Multiple Object Detection Based on Clustering and Deep Learning Methods." Sensors 20, no. 16 (August 7, 2020): 4424. http://dx.doi.org/10.3390/s20164424.
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Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the performance result. The outputs from using deep learning methods on both types of data are treated with K-Means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms to remove outliers, detect and cluster meaningful data, and improve the result of multiple object detections. Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.
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Chen, Ya-Ling, Yan-Rou Cai, and Ming-Yang Cheng. "Vision-Based Robotic Object Grasping—A Deep Reinforcement Learning Approach." Machines 11, no. 2 (February 12, 2023): 275. http://dx.doi.org/10.3390/machines11020275.
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This paper focuses on developing a robotic object grasping approach that possesses the ability of self-learning, is suitable for small-volume large variety production, and has a high success rate in object grasping/pick-and-place tasks. The proposed approach consists of a computer vision-based object detection algorithm and a deep reinforcement learning algorithm with self-learning capability. In particular, the You Only Look Once (YOLO) algorithm is employed to detect and classify all objects of interest within the field of view of a camera. Based on the detection/localization and classification results provided by YOLO, the Soft Actor-Critic deep reinforcement learning algorithm is employed to provide a desired grasp pose for the robot manipulator (i.e., learning agent) to perform object grasping. In order to speed up the training process and reduce the cost of training data collection, this paper employs the Sim-to-Real technique so as to reduce the likelihood of damaging the robot manipulator due to improper actions during the training process. The V-REP platform is used to construct a simulation environment for training the deep reinforcement learning neural network. Several experiments have been conducted and experimental results indicate that the 6-DOF industrial manipulator successfully performs object grasping with the proposed approach, even for the case of previously unseen objects.
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Liu, Li, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen. "Deep Learning for Generic Object Detection: A Survey." International Journal of Computer Vision 128, no. 2 (October 31, 2019): 261–318. http://dx.doi.org/10.1007/s11263-019-01247-4.
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Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.
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Hassan, Ehtesham, Yasser Khalil, and Imtiaz Ahmad. "Learning Feature Fusion in Deep Learning-Based Object Detector." Journal of Engineering 2020 (May 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/7286187.
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Object detection in real images is a challenging problem in computer vision. Despite several advancements in detection and recognition techniques, robust and accurate localization of interesting objects in images from real-life scenarios remains unsolved because of the difficulties posed by intraclass and interclass variations, occlusion, lightning, and scale changes at different levels. In this work, we present an object detection framework by learning-based fusion of handcrafted features with deep features. Deep features characterize different regions of interest in a testing image with a rich set of statistical features. Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. Our detection framework is based on the recent version of YOLO object detection architecture. Experimental evaluation on PASCAL-VOC and MS-COCO datasets achieved the detection rate increase of 11.4% and 1.9% on the mAP scale in comparison with the YOLO version-3 detector (Redmon and Farhadi 2018). An important step in the proposed learning-based feature fusion strategy is to correctly identify the layer feeding in new features. The present work shows a qualitative approach to identify the best layer for fusion and design steps for feeding in the additional feature sets in convolutional network-based detectors.
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Voulodimos, Athanasios, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. "Deep Learning for Computer Vision: A Brief Review." Computational Intelligence and Neuroscience 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/7068349.
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Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.
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Alateeq, Muneerah M., Fathimathul Rajeena P.P., and Mona A. S. Ali. "Construction Site Hazards Identification Using Deep Learning and Computer Vision." Sustainability 15, no. 3 (January 28, 2023): 2358. http://dx.doi.org/10.3390/su15032358.
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Workers on construction sites face numerous health and safety risks. Authorities have made numerous attempts to enhance safety management; yet incidents continue to occur, impacting both worker health and the project’s forward momentum. To that end, developing strategies to improve construction site safety management is crucial. The goal of this project is to employ computer vision and deep learning methods to create a model that can recognize construction workers, their PPE and the surrounding heavy equipment from CCTV footage. Then, the hazards can be discovered and identified based on an analysis of the imagery data and other criteria including weather conditions, and the on-site safety officer can be contacted. Our own dataset was used to train the You Only Look Once model, version 5 (YOLO-v5), which was put to use as an object detection model. The detection model’s performance in tests showed promise for fast and accurate object recognition in the field.
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Hamidisepehr, Ali, Seyed V. Mirnezami, and Jason K. Ward. "Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning." Transactions of the ASABE 63, no. 6 (2020): 1969–80. http://dx.doi.org/10.13031/trans.13791.
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HighlightsCorn damage detection was possible using advanced deep learning and computer vision techniques trained with images of simulated corn lodging.RetinaNet and YOLOv2 both worked well at identifying regions of lodged corn.Automating crop damage identification could provide useful information to producers and other stakeholders from visual-band UAS imagery.Abstract. Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect areas of damaged corn from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which is best suited for corn damage detection. Simulated corn lodging was used to create a training and analysis data set. An unmanned aerial system equipped with an RGB camera was used for image acquisition. Three popular object detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability to detect damaged areas. Average precision (AP) was used to compare object detectors. RetinaNet and YOLOv2 demonstrated robust capability for corn damage identification, with AP ranging from 98.43% to 73.24% and from 97.0% to 55.99%, respectively, across all conditions. Faster R-CNN did not perform as well as the other two models, with AP between 77.29% and 14.47% for all conditions. Detecting corn damage at later growth stages was more difficult for all three object detectors. Keywords: Computer vision, Faster R-CNN, RetinaNet, Severe weather, Smart farming, YOLO.
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Namdev, Utkarsh, Shikha Agrawal, and Rajeev Pandey. "Object Detection Techniques based on Deep Learning: A Review." Computer Science & Engineering: An International Journal 12, no. 1 (February 28, 2022): 125–34. http://dx.doi.org/10.5121/cseij.2022.12113.
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Object detection is a computer technique that searches digital images and videos for occurrences of meaningful subjects in particular categories (such as people, buildings, and automobiles). It is related to computer vision and image processing. Two well-studied aspects of identification are facial and pedestrian detection. Object detection is useful in a wide range of visual recognition tasks, including image retrieval and video monitoring. The object detection algorithm has been improved many times to improve the performance in terms of speed and accuracy. “Due to the tireless efforts of many researchers, deep learning algorithms are rapidly improving their object detection performance. Pedestrian detection, medical imaging, robotics, self-driving cars, face recognition and other popular applications have reduced labor in many areas.” It is used in a wide variety of industries, with applications range from individual safeguarding to business productivity. It is a fundamental component of driver assist systems and driverless cars, which allows automobiles to identify driving lanes and pedestrians to avoid any accidents.
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Murthy, Chinthakindi Balaram, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde, and Zong Woo Geem. "Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review." Applied Sciences 10, no. 9 (May 8, 2020): 3280. http://dx.doi.org/10.3390/app10093280.
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In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions.
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Sultan, Wajeeha, Nadeem Anjum, Mark Stansfield, and Naeem Ramzan. "Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection." Applied Sciences 10, no. 23 (December 7, 2020): 8754. http://dx.doi.org/10.3390/app10238754.
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Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture.
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Balachandran, Venketaramana, Muhammad Nur Aiman Shapiee, Ahmad Fakhri Ab. Nasir, Mohd Azraai Mohd Razman, and Anwar P.P. Abdul Majeed. "Deep Learning Based Human Presence Detection." MEKATRONIKA 2, no. 2 (December 16, 2020): 55–61. http://dx.doi.org/10.15282/mekatronika.v2i2.6768.
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Human detection and tracking have been progressively demanded in various industries. The concern over human safety has inhibited the deployment of advanced and collaborative robotics, mainly attributed to the dimensionality limitation of present safety sensing. This study entails developing a deep learning-based human presence detector for deployment in smart factory environments to overcome dimensionality limitations. The objective is to develop a suitable human presence detector based on state-of-the-art YOLO variation to achieve real-time detection with high inference accuracy for feasible deployment at TT Vision Holdings Berhad. It will cover the fundamentals of modern deep learning based object detectors and the methods to accomplish the human presence detection task. The YOLO family of object detectors have truly revolutionized the Computer Vision and object detection industry and have continuously evolved since its development. At present, the most recent variation of YOLO includes YOLOv4 and YOLOv4 - Tiny. These models are acquired and pre-evaluated on the public CrowdHuman benchmark dataset. These algorithms mentioned are pre-trained on the CrowdHuman models and benchmarked at the preliminary stage. YOLOv4 and YOLOv4 – Tiny are trained on the CrowdHuman dataset for 4000 iterations and achieved a mean Average Precision of 78.21% at 25FPS and 55.59% 80FPS, respectively. The models are further fine-tuned on a Custom CCTV dataset and achieved significant precision improvements up to 88.08% at 25 FPS and 77.70% at 80FPS, respectively. The final evaluation justified YOLOv4 as the most feasible model for deployment.
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Wulandari, Nurcahyani, Igi Ardiyanto, and Hanung Adi Nugroho. "A Comparison of Deep Learning Approach for Underwater Object Detection." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (April 20, 2022): 252–58. http://dx.doi.org/10.29207/resti.v6i2.3931.
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In recent year, marine ecosystems and fisheries becomes potential resources, therefore, monitoring of these objects will be important to ensure their existence. One of computer vision techniques, it is object detection, utilized to recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various deep learning methods implemented in underwater object detection; however, only a few investigations have been performed to compare mainstream object detection algorithms in these circumstances. This article examines various state-of-the-art deep learning methods applied to underwater object detection, including Faster-RCNN, SSD, RetinaNet, YOLOv3, and YOLOv4. We trained five models on RUIE dataset, then the average detection time used to compare how fast a model can detect object within an image; and mAP also applied to measured detection accuracy. All trained models have costs and benefits; SSD was fast but had poor performance; RetinaNet had consistent performance across different thresholds but the detection speed was slow; YOLOv3 was the fastest and had sufficient performance comparable with RetinaNet; YOLOv4 was good at first but performance dropped as threshold enlargement; also, YOLOv4 needed extra time to detect objects compared to YOLOv3. There are no models that are fully suited for underwater object detection; nonetheless, when the mAP and average detection time of the five models were compared, we determined that YOLOv3 is the best acceptable model among the evaluated underwater object detection models.
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Boukerche, Azzedine, and Zhijun Hou. "Object Detection Using Deep Learning Methods in Traffic Scenarios." ACM Computing Surveys 54, no. 2 (April 2021): 1–35. http://dx.doi.org/10.1145/3434398.
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The recent boom of autonomous driving nowadays has made object detection in traffic scenes a hot topic of research. Designed to classify and locate instances in the image, this is a basic but challenging task in the computer vision field. With its powerful feature extraction abilities, which are vital for object detection, deep learning has expanded its application areas to this field during the past several years and thus achieved breakthroughs. However, even with such powerful approaches, traffic scenarios have their own specific challenges, such as real-time detection, changeable weather, and complex lighting conditions. This survey is dedicated to summarizing research and papers on applying deep learning to the transportation environment in recent years. More than 100 research papers are covered, and different aspects such as key generic object detection frameworks, categorized object detection applications in traffic scenario, evaluation metrics, and classified datasets are included. Some open research fields are also provided. We believe that it is the first survey focusing on deep learning-based object detection in traffic scenario.
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Kurniawan, Edi, Hendra Adinanta, Suryadi Suryadi, Bernadus Herdi Sirenden, Rini Khamimatul Ula, Hari Pratomo, Purwowibowo Purwowibowo, and Jalu Ahmad Prakosa. "Deep neural network-based physical distancing monitoring system with tensorRT optimization." International Journal of Advances in Intelligent Informatics 8, no. 2 (July 31, 2022): 185. http://dx.doi.org/10.26555/ijain.v8i2.824.
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During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. PD practices are challenging due to humans' nature as social creatures and the difficulty in estimating the distance from other people. Therefore, some technological aspects are required to monitor PD practices, where one of them is computer vision-based approach. Hence, deep learning-based computer vision is utilized to automatically detect human objects in the video surveillance. In this work, we focus on the performance study of deep learning-based object detector with Tensor RT optimization for the application of physical distancing monitoring system. Deep learning-based object detection is employed to discover people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) and Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. Two different systems-based on Jetson platform are developed as portable devices functioning as PD monitoring stations. The results show that the inferencing speed in regard to Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones, while maintaining the detection accuracies.
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Xu, Ge, Amir Sohail Khan, Ata Jahangir Moshayedi, Xiaohong Zhang, and Yang Shuxin. "The Object Detection, Perspective and Obstacles In Robotic: A Review." EAI Endorsed Transactions on AI and Robotics 1, no. 1 (October 18, 2022): e13. http://dx.doi.org/10.4108/airo.v1i1.2709.
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A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.
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Yang, Kaichen, Tzungyu Tsai, Honggang Yu, Tsung-Yi Ho, and Yier Jin. "Beyond Digital Domain: Fooling Deep Learning Based Recognition System in Physical World." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1088–95. http://dx.doi.org/10.1609/aaai.v34i01.5459.
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Adversarial examples that can fool deep neural network (DNN) models in computer vision present a growing threat. The current methods of launching adversarial attacks concentrate on attacking image classifiers by adding noise to digital inputs. The problem of attacking object detection models and adversarial attacks in physical world are rarely touched. Some prior works are proposed to launch physical adversarial attack against object detection models, but limited by certain aspects. In this paper, we propose a novel physical adversarial attack targeting object detection models. Instead of simply printing images, we manufacture real metal objects that could achieve the adversarial effect. In both indoor and outdoor experiments we show our physical adversarial objects can fool widely applied object detection models including SSD, YOLO and Faster R-CNN in various environments. We also test our attack in a variety of commercial platforms for object detection and demonstrate that our attack is still valid on these platforms. Consider the potential defense mechanisms our adversarial objects may encounter, we conduct a series of experiments to evaluate the effect of existing defense methods on our physical attack.
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I, Ankith. "Real Time Object Detection Using YoloReal Time Object Detection Using Yolo." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1504–11. http://dx.doi.org/10.22214/ijraset.2021.39044.
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Abstract: Object detection is related to computer vision and involves identifying the kinds of objects that have been detected. It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects. Keywords: YOLO, Intersection over Union (IOU), Anchor box, Non-Max Suppression, YOLO application, limitation.
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Mishra, Ranjan Kumar, G. Y. Sandesh Reddy, and Himanshu Pathak. "The Understanding of Deep Learning: A Comprehensive Review." Mathematical Problems in Engineering 2021 (April 5, 2021): 1–15. http://dx.doi.org/10.1155/2021/5548884.
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Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.
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Razzok, Mohammed, Abdelmajid Badri, Ilham EL Mourabit, Yassine Ruichek, and Aıcha Sahel. "Pedestrian detection system based on deep learning." International Journal of Advances in Applied Sciences 11, no. 3 (September 1, 2022): 194. http://dx.doi.org/10.11591/ijaas.v11.i3.pp194-198.
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<p>Pedestrian detection is a rapidly growing field of computer vision with applications in smart cars, surveillance, automotive safety, and advanced robotics. Most of the success of the last few years has been driven by the rapid growth of deep learning, more efficient tools capable of learning semantic, high-level, deeper features of images are proposed. In this article, we investigated the task of pedestrian detection on roads using models based on convolutional neural networks. We compared the performance of standard state-of-the-art object detectors like Faster region-based convolutional network (R-CNN), single shot detector (SSD), and you only look once, version 3 (YOLOv3). Results show that YOLOv3 is the best object detection model than others for pedestrians in terms of detection and time prediction.</p>
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Naik, S. Gopi. "Weapon and Object Detection Using Mobile-Net SSD Model in Deep Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1573–82. http://dx.doi.org/10.22214/ijraset.2021.37622.
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Abstract: The plan is to establish an integrated system that can manage high-quality visual information and also detect weapons quickly and efficiently. It is obtained by integrating ARM-based computer vision and optimization algorithms with deep neural networks able to detect the presence of a threat. The whole system is connected to a Raspberry Pi module, which will capture live broadcasting and evaluate it using a deep convolutional neural network. Due to the intimate interaction between object identification and video and image analysis in real-time objects, By generating sophisticated ensembles that incorporate various low-level picture features with high-level information from object detection and scenario classifiers, their performance can quickly plateau. Deep learning models, which can learn semantic, high-level, deeper features, have been developed to overcome the issues that are present in optimization algorithms. It presents a review of deep learning based object detection frameworks that use Convolutional Neural Network layers for better understanding of object detection. The Mobile-Net SSD model behaves differently in network design, training methods, and optimization functions, among other things. The crime rate in suspicious areas has been reduced as a consequence of weapon detection. However, security is always a major concern in human life. The Raspberry Pi module, or computer vision, has been extensively used in the detection and monitoring of weapons. Due to the growing rate of human safety protection, privacy and the integration of live broadcasting systems which can detect and analyse images, suspicious areas are becoming indispensable in intelligence. This process uses a Mobile-Net SSD algorithm to achieve automatic weapons and object detection. Keywords: Computer Vision, Weapon and Object Detection, Raspberry Pi Camera, RTSP, SMTP, Mobile-Net SSD, CNN, Artificial Intelligence.
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Cantero, David, Iker Esnaola-Gonzalez, Jose Miguel-Alonso, and Ekaitz Jauregi. "Benchmarking Object Detection Deep Learning Models in Embedded Devices." Sensors 22, no. 11 (May 31, 2022): 4205. http://dx.doi.org/10.3390/s22114205.
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Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very high accuracy albeit with high computational costs. Due to the physical limitations of robotic platforms, embedded devices are not as powerful as desktop computers, and adjustments have to be made to deep learning models before transferring them to robotic applications. This work benchmarks deep learning object detection models in embedded devices. Furthermore, some hardware selection guidelines are included, together with a description of the most relevant features of the two boards selected for this benchmark. Embedded electronic devices integrate a powerful AI co-processor to accelerate DL applications. To take advantage of these co-processors, models must be converted to a specific embedded runtime format. Five quantization levels applied to a collection of DL models are considered; two of them allow the execution of models in the embedded general-purpose CPU and are used as the baseline to assess the improvements obtained when running the same models with the three remaining quantization levels in the AI co-processors. The benchmark procedure is explained in detail, and a comprehensive analysis of the collected data is presented. Finally, the feasibility and challenges of the implementation of embedded object detection applications are discussed.
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Feng, Qihan, Xinzheng Xu, and Zhixiao Wang. "Deep learning-based small object detection: A survey." Mathematical Biosciences and Engineering 20, no. 4 (2023): 6551–90. http://dx.doi.org/10.3934/mbe.2023282.
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<abstract> <p>Small object detection (SOD) is significant for many real-world applications, including criminal investigation, autonomous driving and remote sensing images. SOD has been one of the most challenging tasks in computer vision due to its low resolution and noise representation. With the development of deep learning, it has been introduced to boost the performance of SOD. In this paper, focusing on the difficulties of SOD, we analyze the deep learning-based SOD research papers from four perspectives, including boosting the resolution of input features, scale-aware training, incorporating contextual information and data augmentation. We also review the literature on crucial SOD tasks, including small face detection, small pedestrian detection and aerial image object detection. In addition, we conduct a thorough performance evaluation of generic SOD algorithms and methods for crucial SOD tasks on four well-known small object datasets. Our experimental results show that network configuring to boost the resolution of input features can enable significant performance gains on WIDER FACE and Tiny Person. Finally, several potential directions for future research in the area of SOD are provided.</p> </abstract>
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Wang, Ningwei, Yaze Li, and Hongzhe Liu. "Reinforced Neighbour Feature Fusion Object Detection with Deep Learning." Symmetry 13, no. 9 (September 3, 2021): 1623. http://dx.doi.org/10.3390/sym13091623.
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Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, previous works have tried to improve the performance in various object detection necks but have failed to extract features efficiently. To solve the insufficient features of objects, this work introduces some of the most advanced and representative network models based on the Faster R-CNN architecture, such as Libra R-CNN, Grid R-CNN, guided anchoring, and GRoIE. We observed the performance of Neighbour Feature Pyramid Network (NFPN) fusion, ResNet Region of Interest Feature Extraction (ResRoIE) and the Recursive Feature Pyramid (RFP) architecture at different scales of precision when these components were used in place of the corresponding original members in various networks obtained on the MS COCO dataset. Compared to the experimental results after replacing the neck and RoIE parts of these models with our Reinforced Neighbour Feature Fusion (RNFF) model, the average precision (AP) is increased by 3.2 percentage points concerning the performance of the baseline network.
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Mauri, Antoine, Redouane Khemmar, Benoit Decoux, Nicolas Ragot, Romain Rossi, Rim Trabelsi, Rémi Boutteau, Jean-Yves Ertaud, and Xavier Savatier. "Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility." Sensors 20, no. 2 (January 18, 2020): 532. http://dx.doi.org/10.3390/s20020532.
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In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOv3 which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 for monocular vision and MADNEt for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time conditions. Object tracking is necessary in order to mitigate the risks of collisions. Unlike traditional tracking approaches which require target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT approach for 3D object tracking. We introduce an extended Kalman filter to better estimate the position of objects. Extensive experiments carried out on KITTI dataset prove that our proposal outperforms state-of-the-art approches.
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Hidayat, Rahmat, Hendrick, Riandini, Zhi-Hao Wang, and Horng Gwo-Jiun. "Mask RCNN Methods for Eyes Modelling." International Journal of Data Science 2, no. 2 (December 31, 2021): 63–68. http://dx.doi.org/10.18517/ijods.2.2.63-68.2021.
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Object detection is one of Deep Learning section in Computer Vision. The application of computer vision is divided into image classification and object detection. Object detection have target to find specific object from an image. The application of object detection for security are face recognition, and face detection. Face detection have been developed for medical application to identify emotion from faces. In this research, we proposed an eye modelling by using Mask RCNN. The eye model was applied in real time detection combined with OpenCV. The dataset was created from online dataset and image from webcam. The model was trained with 4 epochs and 131 iterations. The final model was successfully detected eye from real-time application.
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Turchini, Francesco, Lorenzo Seidenari, Tiberio Uricchio, and Alberto Del Bimbo. "Deep Learning Based Surveillance System for Open Critical Areas." Inventions 3, no. 4 (October 11, 2018): 69. http://dx.doi.org/10.3390/inventions3040069.
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How to automatically monitor wide critical open areas is a challenge to be addressed. Recent computer vision algorithms can be exploited to avoid the deployment of a large amount of expensive sensors. In this work, we propose our object tracking system which, combined with our recently developed anomaly detection system. can provide intelligence and protection for critical areas. In this work. we report two case studies: an international pier and a city parking lot. We acquire sequences to evaluate the effectiveness of the approach in challenging conditions. We report quantitative results for object counting, detection, parking analysis, and anomaly detection. Moreover, we report state-of-the-art results for statistical anomaly detection on a public dataset.
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Dharmik, R. C., Sushilkumar Chavhan, and S. R. Sathe. "Deep learning based missing object detection and person identification: an application for smart CCTV." 3C Tecnología_Glosas de innovación aplicadas a la pyme 11, no. 2 (December 29, 2022): 51–57. http://dx.doi.org/10.17993/3ctecno.2022.v11n2e42.51-57.
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Security and protection are the most crucial concerns in today’s quickly developing world. Deep Learning methods and computer vision assist in resolving both problems. One of the computer vision subtasks that allows us to recognise things is object detection. Videos are a source that is taken into account for detection, and image processing technology helps to increase the effectiveness of state-ofthe-art techniques. With all of these technologies, CCTV is recognised as a key element. Using a deep convolutional neural network, we accept CCTV data in real time in this article. The main objective is to make content the centre of things. Using the YOLO technique, we were able to detect the missing item with an improvement of 10% sparsity over the current state-of-the-art algorithm in the context of surveillance systems, where object detection is a crucial step. It can be utilised to take immediate additional action.
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Abbas, Touqeer, Abdul Razzaq, Muhammad Azam Zia, Imran Mumtaz, Muhammad Asim Saleem, Wasif Akbar, Muhammad Ahmad Khan, Gulzar Akhtar, and Casper Shikali Shivachi. "Deep Neural Networks for Automatic Flower Species Localization and Recognition." Computational Intelligence and Neuroscience 2022 (April 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/9359353.
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Deep neural networks are efficient methods of recognizing image patterns and have been largely implemented in computer vision applications. Object detection has many applications in computer vision, including face and vehicle detection, video surveillance, and plant leaf detection. An automatic flower identification system over categories is still challenging due to similarities among classes and intraclass variation, so the deep learning model requires more precisely labeled and high-quality data. In this proposed work, an optimized and generalized deep convolutional neural network using Faster-Recurrent Convolutional Neural Network (Faster-RCNN) and Single Short Detector (SSD) is used for detecting, localizing, and classifying flower objects. We prepared 2000 images for various pretrained models, including ResNet 50, ResNet 101, and Inception V2, as well as Mobile Net V2. In this study, 70% of the images were used for training, 25% for validation, and 5% for testing. The experiment demonstrates that the proposed Faster-RCNN model using the transfer learning approach gives an optimum mAP score of 83.3% with 300 and 91.3% with 100 proposals on ten flower classes. In addition, the proposed model could identify, locate, and classify flowers and provide essential details that include flower name, class classification, and multilabeling techniques.
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Gundu, Sireesha, and Hussain Syed. "Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques." Sensors 23, no. 5 (February 25, 2023): 2569. http://dx.doi.org/10.3390/s23052569.
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Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset.
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Ullah, Habib, Mohib Ullah, Sultan Daud Khan, and Faouzi Alaya Cheikh. "EVALUATING DEEP SEMI-SUPERVISED LEARNING METHODS FOR COMPUTER VISION APPLICATIONS." Electronic Imaging 2021, no. 6 (January 18, 2021): 313–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.6.iriacv-313.
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Deep semi-supervised learning (SSL) have been significantly investigated in the past few years due to its broad spectrum of theory, algorithms, and applications. The extensive use of the SSL methods is dominant in the field of computer vision, for example, image classification, human activity recognition, object detection, scene segmentation, and image generation. In spite of the significant success achieved in these domains, critically analyzing SSL methods on benchmark datasets still presents important challenges. In the literature, very limited reviews and surveys are available. In this paper, we present short but focused review about the most significant SSL methods. We analyze the basic theory of SSL and the differences among various SSL methods. Then, we present experimental analysis to compare these SSL methods using standard datasets. We also provide an insight into the challenges of the SSL methods.
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Varadarajan, Vijayakumar, Dweepna Garg, and Ketan Kotecha. "An Efficient Deep Convolutional Neural Network Approach for Object Detection and Recognition Using a Multi-Scale Anchor Box in Real-Time." Future Internet 13, no. 12 (November 29, 2021): 307. http://dx.doi.org/10.3390/fi13120307.
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Deep learning is a relatively new branch of machine learning in which computers are taught to recognize patterns in massive volumes of data. It primarily describes learning at various levels of representation, which aids in understanding data that includes text, voice, and visuals. Convolutional neural networks have been used to solve challenges in computer vision, including object identification, image classification, semantic segmentation and a lot more. Object detection in videos involves confirming the presence of the object in the image or video and then locating it accurately for recognition. In the video, modelling techniques suffer from high computation and memory costs, which may decrease performance measures such as accuracy and efficiency to identify the object accurately in real-time. The current object detection technique based on a deep convolution neural network requires executing multilevel convolution and pooling operations on the entire image to extract deep semantic properties from it. For large objects, detection models can provide superior results; however, those models fail to detect the varying size of the objects that have low resolution and are greatly influenced by noise because the features after the repeated convolution operations of existing models do not fully represent the essential characteristics of the objects in real-time. With the help of a multi-scale anchor box, the proposed approach reported in this paper enhances the detection accuracy by extracting features at multiple convolution levels of the object. The major contribution of this paper is to design a model to understand better the parameters and the hyper-parameters which affect the detection and the recognition of objects of varying sizes and shapes, and to achieve real-time object detection and recognition speeds by improving accuracy. The proposed model has achieved 84.49 mAP on the test set of the Pascal VOC-2007 dataset at 11 FPS, which is comparatively better than other real-time object detection models.
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Hassan, Adel, and Muath Sabha. "Feature Extraction for Image Analysis and Detection using Machine Learning Techniques." International Journal of Advanced Networking and Applications 14, no. 04 (2023): 5499–508. http://dx.doi.org/10.35444/ijana.2023.14401.
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Feature extraction is the most vital step in image classification to produce high-quality and good content images for further analysis, image detection, segmentation, and object recognition. Using machine learning algorithms, profound learning like convolutional neural network CNN became necessary to train, classify, and recognize images and objects like humans. Combined feature extraction and machine learning classification to locate and identify objects on images can then be an input of automatic recognition systems ATR such as surveillance systems CCTV, to enhance these systems and reduce time and effort for object detection and recognition in images based on digital image processing techniques especially image segmentation that differentiate from computer vision approach. This article will use machine learning and deep learning algorithms to facilitate and achieve the study's objectives.