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作者： Li, Juntao Zhao, Meijuan Qin, Zhenbo Yuan, Ruiping Huang, Anqiang Li, Mengtao School of Information Beijing Wuzi University Beijing101149 China Beijing Key Laboratory of Intelligent Logistics Systems Beijing101149 China School of Economics and Management Beijing Jiaotong University Beijing100044 China School of Business Administration Northeast University of Finance and Economics Dalian116025 China

Different forms of two-dimensional codes are already widely used in all aspects of people’s production and life, and the demand for rapid detection and recognition technology of multiple QR codes in various complex scenarios is also increasing. The traditional QR code detection algorithm has a high miss detection rate. In this paper, we collected a little sample data of multiple QR codes and annotated them. We used the yolov3 and yolov5 algorithms to implement the detection of multiple QR codes. Then, we added CBAM to the YOLO algorithm, and added an angle prediction mechanism to improve their decoding and recognition effects. The experimental results showed that the yolov3_CBAM algorithm and the yolov5_CBAM algorithm improved by 0.63% and 8.89% about mAP@.5 and mAP@.5:.95 respectively for the multiple QR codes dataset, with a detection speed of 70 FPS and achieved real-time performance. © 2024 Inderscience Enterprises Ltd.

关键词： angle prediction attention mechanism detection and recognition of multiple QR codes yolov3 algorithm yolov5 algorithm

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Study on the Detection of Missing Insulators Based on Deep Learning algorithm

Study on the Detection of Missing Insulators Based on Deep L...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Wang, Sen Sun, Peipei Liang, Daili Fan, Zhihan Xiao, Yanhui Shenyang Institute of Engineering Department of College of Automation Shenyang110000 China Shenyang Institute of Engineering Department of College of Electric Shenyang110000 China

ISBN: (纸本)9798350301571

An missing insulators detection method based on a deep learning algorithm is proposed to identify missing insulators quickly and accurately. The Mish function and the dilated convolution method of the deep learning algorithm are used to improve the effectiveness of missing insulators detection. Missing insulators detection is one of the essential measures to ensure the safe and economic operation of the power system. Furthermore, the proposed detection method can be effectively used to detect missing insulators in high-voltage transmission lines in this paper. The results show that the object detection of the deep learning algorithm is used to complete the identification of missing insulators. The mAP is 99.7%, the recall rate is 99.2%, and the precision rate is 99.6%, which is 3.1% higher in mAP, 3.4% higher in the recall, and 1.3% higher in precision compared to the original deep learning algorithm studied in this paper. This deep learning algorithm is a superior performance model. This provides a new opportunity for real-time insulator fault detection on mobile terminals. © 2023 IEEE.

关键词： dilated Convolution Mish function missing insulator yolov3 algorithm

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Faster Region-based Convolutional Neural Networks with You Only Look Once multi-stage caries lesion from oral panoramic X-ray images

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EXPERT SYSTEMS 2024年第6期41卷 e13326-e13326页

作者： Velusamy, Jayaraj Rajajegan, T. Alex, Sini Anna Ashok, M. Mayuri, A. V. R. Kiran, Siripuri Nehru Inst Engn & Technol Dept Elect & Commun Engn Coimbatore India KPR Inst Engn & Technol Dept CSE Coimbatore India Ramaiah Inst Technol Dept Comp Sci & Engn AI & ML Bangalore India Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai India VIT Bhopal Univ Sch Comp Sci & Engn Sehore India Kakatiya Inst Technol & Sci Dept Comp Sci & Engn Networks Warangal India

Neural networks and artificial intelligence find more applications in dentistry. It treats dental caries, the most prevalent type of dental illness worldwide. Even though dental caries can be prevented and treated, they typically cause dental discomfort and tooth loss. For dental caries to be treated quickly and effectively, comprehensive detection may be needed, a combination of techniques that include eye inspection, probing, using a dental probe, and using a hand-held mirror, and the individual application of each of these techniques, can quickly identify large caries cavities. Long-established caries detection techniques help to locate only partially hidden but still accessible holes. Deep learning (DL) techniques have produced remarkable diagnostic results in radiology. This study aimed to classify various radiographic extensions on panoramic films using DL techniques, identify caries lesions using these techniques, and compare the results to those of dentists with extensive training. Faster region-based convolutional neural networks (R-CNN) is a newly discovered field of medical research that is rapidly expanding and has produced outstanding results in diagnosing and prognosis of pathology and radiology conditions. In this study, dental cavities were detected and analysed using periapical radiographs to evaluate the accuracy of the Faster R-CNN algorithm. Because these three caries were derived from the oral panoramic images, we designed You Only Look Once Version 3 (yolov3) as a U-shaped network with a large-scale axial attention module. We also compare the effectiveness of yolov3's segmentation to that of other industrial standards. Experiments show that our proposed method, Fast R-CNN-yolov3, achieves higher accuracy in segmenting the three distinct caries level. The proposed model (R-CNN-yolov3) achieved an effective result with a precision of 97.183%.

关键词： dental caries image recognition machine learning oral images yolov3 algorithm

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Garbage Detection algorithm Based on YOLO v3

Garbage Detection Algorithm Based on YOLO v3

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IEEE International Conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Liu Bohong Wang Xinpeng Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9781665416061

In recent years, garbage classification has become the focus of the world. yolov3 has good real-time performance and accurate detection accuracy, which can meet the requirements of garbage sorting equipment. In this paper, we introduce a lightweight cross-channel interaction Attention mechanism in the residual unit of yolov3, and propose a new target detection algorithm, namely Efficient Channel Attention YOLO (ECA-YOLO) detection algorithm. So the target detection algorithm can get more complete and more effective feature information. Experimental results show that the improved model performs better than yolov3 in the garbage image data set of "Huawei Garbage Classification Challenge Cup". The improved model has a high accuracy and recall rate, and the detection mAP is improved by 1.07%, while the detection speed is kept unchanged and the number of parameters is introduced.

关键词： object detection garbage classification deep learning yolov3 algorithm

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Deep Learning-Based Object Detection Improvement for Fine-Grained Birds

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IEEE ACCESS 2021年 9卷 67901-67915页

作者： Yang, Kuihe Song, Ziying Hebei Univ Sci & Technol Sch Informat Engn & Technol Shijiazhuang 050018 Hebei Peoples R China

When the object detection algorithm is applied to the bird protection project, there are many problems like large model parameters, high similarity between bird species and single sample scene. In order to further improve the detection accuracy and stability of the object detection model, a multi-object detection algorithm for fine-grained birds is proposed. Firstly, the algorithm introduces Depthwise separable convolution into the feature extraction layer of yolov3 algorithm. The convolution process is divided into two parts: deep convolution and point-by-point convolution. The separation between intra-channel convolution and inter-channel convolution is realized. On the basis of high detection accuracy, the number of algorithm model parameters and calculation amount are greatly reduced. Finally, Focal loss was added to the loss function to solve the serious imbalance of positive and negative samples. By reducing the weight of the large number of simple background classes, the algorithm was more focused on detecting foreground classes. The experimental results show that, in the bird data set, the average precision mean (mAP) index of this algorithm is 2.71% higher than yolov3 algorithm, the number of parameters is 79.88% lower than yolov3 basic model, and the number of frames per second (FPS) is 19.98% higher than yolov3 algorithm. This algorithm not only greatly reduces the number of model parameters and computation, but also improves the detection speed and mAP.

关键词： Birds Convolution Object detection Training Detection algorithms Classification algorithms Feature extraction yolov3 algorithm Depthwise separable convolution Focal loss object detection

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Multi-target trajectory tracking in multi-frame video images of basketball games based on deep learning

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第2期10卷

作者： Gong, Yong Srivastava, Gautam Wuhan Polytech Univ Minist Sport Wuhan 430048 Peoples R China Brandon Univ Dept Math & Comp Sci Brandon MB Canada

INTRODUCTION: There is occlusion interference in the multi-target visual tracking process of basketball video images, which leads to poor accuracy of multi-target trajectory tracking. This paper studies the multi-target trajectory tracking method in multi-frame video images of basketball sports based on deep learning. OBJECTIVES: Aiming at the problem of target occlusion in the tracking process and the problem of trajectory tracking anomaly caused by target occlusion, a modified algorithm is proposed. METHODS: The method is divided into two parts: detection and tracking. In the detection part, the yolov3 algorithm in deep learning technology is used to detect each target in the video, and the original yolov3 backbone network Darknet53 is replaced by the lightweight backbone network MobileNetV2 to extract the target features. RESULTS: Based on the target detection results, the Kalman filter is used to predict the next position and bounding box size of the target to obtain the target trajectory prediction results according to the current target position, then a hierarchical data association algorithm is designed, and multi-target tracking of the same category is completed based on the target appearance feature similarity and feature similarity. CONCLUSION: The experimental results show that the method can accurately detect the targets in multi-frame video images in basketball sports and obtain high-precision target trajectory tracking results.

关键词： deep learning basketball sports video multi -objective trajectory tracking yolov3 algorithm data association

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Deep Learning Based Face Mask Detection and Crowd Counting 6

Deep Learning Based Face Mask Detection and Crowd Counting

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6th International Conference for Convergence in Technology (I2CT)

作者： Amin, Prithvi N. Moghe, Sayali S. Prabhakar, Sparsh N. Nehete, Charusheela M. VESIT Informat Technol Mumbai Maharashtra India

ISBN: (纸本)9781728188768

Without any uncertainty, the COVID-19 pandemic has set the world to a stop. The infection is spreading rapidly and could be a peril to the human race. Seeing the prerequisite of the hour, one needs to continuously make certain safeguards of that one being wearing a mask in any way at all times. To make a secure environment that leads to open safety, we propose a proficient learning and computer vision based approach concentrated on the real-time robotized observation of individuals to find unmasked faces in open areas. Subsequently, the recommended solution favors the society by sparing time and helps in bringing down the spread of coronavirus. It can be implemented successfully when lockdown is lifted completely bringing about people in open get-togethers, shopping centers, etc.. Automated inspection will reduce manpower to oversee the public and can also be used in any place.

关键词： Deep Learning yolov3 algorithm MobileNet-SSD algorithm

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Research on belt foreign body detection method based on deep learning

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2022年第15期44卷 2919-2927页

作者： Xiao, Dong Kang, Zhuang Yu, Hang Wan, Lushan Northeastern Univ Coll Informat Sci & Engn 3-11 Wenhua Rd Shenyang 110819 Peoples R China Harbin Inst Technol Sch Astronaut Harbin Peoples R China

Recently, accelerating the improvement in the level of iron ore mining has become extremely important for the sustainable development of the steel industry. Various foreign objects such as steel bars, wood, and plastic pipes easily appear on iron ore belt conveyors, which cause damage to equipment and harm the personal safety of mining workers. Accordingly, a lightweight detection model based on you only look once (YOLO)v3 is proposed in this study. This study first uses the median filter method to preprocess the belt foreign body image to remove the influence of dust and improve the clarity of ore edges. Second, we train the yolov3 belt foreign matter detection algorithm based on the selected data set to detect belt foreign matter and evaluate the model based on mean average precision (mAP) and other indicators. Finally, after implementing the sparse training based on the batch normalization (BN) layers, the channel-pruning and layer-pruning strategies are implemented to simplify the yolov3 model, followed by parameter fine-tuning. When the accuracy of the model is not affected, our model realizes smaller calculations, faster processing, and a smaller size compared to the original yolov3 model. Hence, the model effectively achieves real-time recognition of foreign matter on the ore conveyor belt.

关键词： yolov3 algorithm belt foreign body recognition model compression channel pruning layer pruning

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Insulator Detection Dased on GIOU-yolov3

Insulator Detection Dased on GIOU-YOLOv3

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Chinese Automation Congress (CAC)

作者： Lina Yao Qin Yaoyao Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176871

Insulator is an important power equipment component and plays an important role in power *** view of the problem that the self-destructive damage of the insulator may cause the transmission line fault, the method that GIOU-yolov3 insulator detection and positioning is proposed. Compared with the IOU loss used in the traditional anchor-based detection method in yolov3, an insulator detection method which replaces the original loss function with the "GloU loss function" in this paper. Without increasing the size of model, the detection accuracy of the insulator is improved. In the subsequent comparative experiment, the effectiveness of this method is verified.

关键词： deep learning yolov3 algorithm GIoU loss function insulator

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Mask detection device based on yolov3 framework 5

Mask detection device based on YOLOv3 framework

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： He, Jianwen Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781665423144

In early 2020, novel coronavirus pneumonia broke out. In order to prevent the spread of the disease, governments around the world asked the masses to wear masks. However, there are still many people who do not wear masks in public places. To solve this problem, this paper proposes a mask detection device based on yolo3 framework. The device uses the yolov3 algorithm to extract the face prediction area, and uses the gray image to calculate the skin exposure rate of the mouth and nose of the face, so as to judge whether the recognized person is wearing a mask or not and whether the mask is wearing correctly. The model is deployed on the hardware to facilitate the staff to carry the detection. The experimental results show that the recognition rate is 86.6%.

关键词： yolov3 algorithm Grayscale image Neural network Mask recognition

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