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Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using yolov3 algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第6期82卷 8381-8403页

作者： Kumar, Saurav Yadav, Drishti Gupta, Himanshu Kumar, Mohit Verma, Om Prakash Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India Tech Univ Wien Fac Informat A-1040 Vienna Austria Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Instrumentat & Control Engn Jalandhar 144011 Punjab India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Informat & Technol Jalandhar 144011 Punjab India

The eruption of COVID-19 pandemic has led to the blossoming usage of face masks among individuals in the communal settings. To prevent the transmission of the virus, a mandatory mask-wearing rule in public areas has been enforced. Owing to the use of face masks in communities at different workplaces, an effective surveillance seems essential because several security analyses indicate that face masks may be used as a tool to hide the identity. Therefore, this work proposes a framework for the development of a smart surveillance system as an aftereffect of COVID-19 for recognition of individuals behind the face mask. For this purpose, transfer learning approach has been employed to train the custom dataset by yolov3 algorithm in the Darknet neural network framework. Moreover, to demonstrate the competence of yolov3 algorithm, a comparative analysis with yolov3-tiny has been presented. The simulated results verify the robustness of yolov3 algorithm in the recognition of individuals behind the face mask. Also, yolov3 algorithm achieves a mAP of 98.73% on custom dataset, outperforming yolov3-tiny by approximately 62%. Moreover, yolov3 algorithm provides adequate speed and accuracy on small faces.

关键词： COVID-19 Facemask detection Object detection Surveillance system yolov3 algorithm Convolutional neural network Deep neural networks

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Application of Mathematical Statistics Method Based on yolov3 algorithm in Measurement and Detection Data

Application of Mathematical Statistics Method Based on YOLOv...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Wang, Yunfan Zhongtai Securities Beijing China

ISBN: (纸本)9798350313413

With the development of Internet visual algorithms, the target demand of measurement and detection data analysis is optimized constantly. In the view of wide application of target detection technology in various industries, mathematical statistics based on yolov3 (You Only Look Once) algorithm is increasingly important for the analysis of network detection data in different industries. In the construction of the entire target metric detection model, how to enhance accuracy of data detection and decrease model data processing time is currently significant issue that requires to be instantly resolved. This article studied the optimization model of target detection using the yolov3 algorithm and the analysis of metric detection, aiming to enhance the accuracy and efficiency of model detection. In this paper, through the simulation experiment analysis of COCO (Common Objects in Context) dataset, the mathematical statistics method based on yolov3 algorithm was applied to research, and the flow chart design was optimized by combining multi-scale improvement and loss function. Based on the discussion of data results, the following conclusion can be drawn. In the context of internet big data, optimization model for object detection using yolov3 algorithm has improved detection accuracy in the four sampled test domain parameters, and overall average improvement is 6%. During this time, there have been improvements in detection efficiency, and the overall average improvement is 10.25%. This shows that the mathematical statistics method based on yolov3 algorithm has a good practical application effect in the measurement and detection data analysis. This method combines deep learning technology and mathematical statistical methods to make the analysis of metrological detection data more robust, and can cope with complex environmental conditions and diverse measurement targets. It is expected to play a key role in fields such as industrial quality inspection, traffic monitoring,

关键词： Data Analysis Metrological Detection Simulation Experiments yolov3 algorithm

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Smart Classroom Surveillance System Using yolov3 algorithm 1

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3rd International Conference on Recent Innovations and Technological Development in Mechanical Engineering, ICRITDME 2020

作者： Kumar, Saurav Yadav, Drishti Gupta, Himanshu Verma, Om Prakash Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar Jalandhar India

ISBN: (数字)9789811692369

ISBN: (纸本)9789811692352

One of the major concerns associated with educational institutions is the attendance survey, monitoring, and surveillance. Owing to the labor-intensive nature of manual attendance system involving the management of attendance records, the current focus is on the emergence of an efficient and accurate attendance system. This paper presents a maiden attempt to propose a smart classroom attendance and surveillance system using yolov3 algorithm, a novel deep learning approach. An attempt has been made to avoid the unnecessary wastage of time spent during attendance marking and also to avoid fake attendance. Using yolov3 algorithm in the DarkNet framework, a realistic dataset of images with around 14 students and faculty members has been used to train the test model. The dataset has been formed by acquiring the realistic images from the Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India. The test results demonstrate the efficiency of yolov3 algorithm in effective face recognition, thereby endorsing its capability and usage in smart classroom surveillance system. In addition, the performance of yolov3 has been compared with yolov3-tiny algorithm to validate its robustness and competence in classroom surveillance tasks. The experimental results demonstrate a maximum accuracy of 99% by yolov3 algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Attendance system Classroom surveillance Deep learning yolov3 algorithm yolov3-tiny algorithm

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Underwater targets detection and classification in complex scenes based on an improved yolov3 algorithm

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JOURNAL OF ELECTRONIC IMAGING 2020年第4期29卷 p43013-43013页

作者： Shi, Tingchao Liu, Mingyong Niu, Yun Yang, Yang Huang, Yuxuan Northwestern Polytech Univ Sch Marine Sci & Engn Sci & Technol Underwater Informat & Control Lab Xian Peoples R China Northwestern Polytech Univ Sch Marine Sci & Engn Underwater Vehicle Res Inst Xian Peoples R China

The fast detection and classification of underwater targets is a key issue in the operation of intelligent underwater robots. In order to improve the detection speed of underwater targets and reduce the missed detection rate of small targets, an improved yolov3 algorithm named yolov3-Marine is proposed. The network parameters were reduced and the detection speed was increased due to improving the yolov3 network structure. The residual module was optimized to improve the feature extraction capabilities of the network, which greatly reduced the rate of missed detection in the case of densely distributed targets. Finally, the prediction scale module and the loss function were improved to increase the detection accuracy of small underwater targets. The final experimental results showed that the proposed yolov3-Marine algorithm has a higher detection speed and detection accuracy than the yolov3 algorithm. (C) 2020 SPIE and IS&T

关键词： underwater targets detection classification yolov3 algorithm complex scenes

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Lameness detection of dairy cows based on the yolov3 deep learning algorithm and a relative step size characteristic vector

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BIOSYSTEMS ENGINEERING 2020年 189卷 150-163页

作者： Wu, Dihua Wu, Qian Yin, Xuqiang Jiang, Bo Wang, Han He, Dongjian Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn 22 Xinong Rd Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

Intelligent lameness detection is important for improving cow welfare. A method based on yolov3 deep learning algorithm and relative step size characteristic vector is proposed to classify lame and non-lame cows. Videos were decomposed into sequence frames, and leg targets of cows in each frame were detected by yolov3 algorithm. Relative step sizes of cow's front and rear legs were calculated based on leg coordinates, and the relative step size characteristic vector was constructed. Finally, a trained Long Short-Term Memory (LSTM) classification model was used to classify lame and non-lame cows based on the characteristic vector. A total of 210 videos were selected for verification using LSTM, support vector machine (SVM), K-Nearest Neighbour (KNN) and decision tree classifier (DTC) algorithms. Results showed that accuracy of lameness detection based on LSTM was 98.57%, which was 2.93%, 3.88%, and 9.25% higher than SVM, KNN, and DTC, respectively. True positive rate of the LSTM was 0.97, which was 0.03, 0.04 and 0.06 higher than SVM, KNN and DTC, respectively. False positive rate of LSTM was 0.03, which was 0.03, 0.06 and 0.11 lower than SVM, KNN and DTC, respectively. A bidirectional LSTM performed slightly better than LSTM but would be more demanding on hardware. Comparison of LSTM with a purely deep learning method showed the latter performed slightly better, but was less conducive to interpretation and diagnosis of lameness. The relative step size characteristic vector proposed was effective for classification of lame and non-lame cows, and could lead to intelligent detection of lameness. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Dairy cow Lameness detection yolov3 algorithm Relative step size characteristic vector LSTM

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Real-time object detection and tracking using yolov3 network by quadcopter

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2024年第4期52卷 1811-1829页

作者： Mokhtari, Mir Abolfazl Taheri, Mehrdad Univ Imam Ali Tehran Iran Univ Amirkabir Tehran Iran

In this article, artificial intelligence is applied for real-time object detection in Tello quadcopters. For this purpose, the yolov3 detection algorithm as a highly used deep-learning method is employed. The results indicate that the yolov3 network can be trained with an accuracy of 99 percent and can detect the target with above 95 percent accuracy at a speed of 15 frames-per-second for different ambient lighting and background conditions. The yolov3 algorithm is trained using a custom dataset and implemented in Python. Images are sent to a computer using Python language to detect the target and entered into the yolov3 algorithm. After detecting the target, the errors are calculated and given to the control system to track the target in real-time. There are two purposes in tracking, the target is in the view of the quadcopter, and the quadcopter is at a certain distance from the target. For this purpose, the effect of quadcopter movements on the coordinates and area of the target is examined and four controllers are designed to follow the target and keep the robot at a certain distance from the target. The designed controllers efficiently follow the target and prevent flying robots from losing sight of the target.

关键词： Flying robot yolov3 algorithm object tracking target detection

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Track Obstacle Detection algorithm Based on yolov3 13

Track Obstacle Detection Algorithm Based on YOLOv3

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13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Cong, Zijian Li, Xiaoguang Changchun Univ Sch Elect Informat Engn Changchun Peoples R China Changchun Guanghua Univ Sch Elect Informat Changchun Peoples R China

ISBN: (纸本)9780738105451

In this paper, yolov3 algorithm is applied to track obstacle detection to achieve the robustness of detection. Firstly, the network structure and principle of yolov3 algorithm are introduced. Then, the boundary box prediction function and category prediction of multi-scale target detection of yolov3 are analyzed. Finally, the implementation steps of yolov3 are given. Experimental results show that, compared with yolov2 algorithm,yolov3 algorithm has better robustness, more targets can be detected and positioning is more accurate in the case of complex background with good lighting and low illumination contrast. In the case of more pedestrians in front of the track in a good light environment, the number of detected targets increased from 4 to 8, and the average prediction probability of the target category increased from 0.673 to 0.978. For the complex background with human-vehicle mixture, the average prediction probability increased from 0.72 to 0.94. In the case of poor illumination environment in rainy dusk, the detection target increased from 2 cars to 4 cars and 1 pedestrian, and the average forecast probability increased from 0.51 to 0.77. For the small target image of two pedestrians in the distance in front of the track,yolov2 algorithm cannot detect the target, while yolov3 algorithm can accurately detect the target.

关键词： Target detection yolov3 algorithm Track obstacle detection

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High-accuracy Object Detection Based on yolov3 Under Different Weather Conditions

High-accuracy Object Detection Based on YOLOv3 Under Differe...

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International Conference on Big Data, Information and Computer Network (BDICN)

作者： Wu, Runxun Wang, Bingyuan Guo, Xingchen Beijing Univ Posts & Telecommun Int Sch Beijing Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Qingdao Univ Sch Elect Engn Qingdao Peoples R China

ISBN: (纸本)9781665484763

To improve the accuracy and speed of image target detection under complex weather conditions, an image target detection method based on the yolov3 algorithm and image enhancement processing is proposed. Firstly, we make a data set specifically for image target detection by filtering the VOC 2007 data set. Then we select the yolov3 target detection model as the infrastructure network of image target detection. We improved the network structure for better accuracy. Then, the original data is enhanced by setting different atmospheric and channel conditions, and we use the improved yolov3 model to detect the image target and calculate the accuracy. Experimental results show that the system has good prediction accuracy for all 20 categories in the original data set. In addition, the image object detection method based on the combination of the yolov3 algorithm and image enhancement processing can effectively complete the task of image object detection under complex weather conditions.

关键词： Deep learning Image target detection yolov3 algorithm Weather environment Channel environment

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Arduino-based Line Tracking Smart Robot Car Equipped with yolov3 and Fuzzy Logic Controlled Ultraviolet Sterilizer 8

Arduino-based Line Tracking Smart Robot Car Equipped with YO...

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8th International Conference on Automation, Robotics and Applications (ICARA)

作者： Garcia, Ramon G. Manuel, Levin D. Romero, Cesar Quartz L., Jr. Mapua Univ Sch EECE Manila Philippines

ISBN: (纸本)9781665483834

With the increasing number of cases of pathogenic diseases caused by airborne transmission of microorganisms, such as Staphylococcus aureus, interest when it comes to the disinfection of surfaces is increasing. One of the many devices that have been utilized for surface disinfection is the use of Ultraviolet Light radiation, specifically UVC radiation, as a sterilizer or disinfectant. In this study, a UVC disinfecting machine based on Arduino Nano was developed with a mobile application to control and enable the device's functionalities. The UVC disinfecting machine can be manually controlled via the generated mobile application and automatically navigated using the Line Tracking algorithm. To ensure the safety of the people utilizing the device, the UVC disinfecting machine will be encircled with several motion sensors and a Wi-Fi camera module. The sensors and camera automatically disable its UVC lamp once it detects human presence by applying the algorithms of Fuzzy Logic and yolov3. This study provides a versatile, safe, and harmless UVC disinfecting machine while effectively disinfecting objects and surfaces.

关键词： staphylococcus Aureus ultraviolet light UVC radiation disinfecting machine Arduino Nano mobile application line tracking algorithm motion sensors Wi-Fi Camera Module fuzzy logic algorithm yolov3 algorithm

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Detection and recognition of stationary vehicles and seat belts in intelligent Internet of Things traffic management system

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NEURAL COMPUTING & APPLICATIONS 2022年第5期34卷 3513-3522页

作者： Wang, Zhenyan Ma, Yongjie Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Gansu Peoples R China

The increase in the size of the city and the increase in population mobility have greatly increased the number of vehicles on the road, and at the same time brought considerable challenges to the traffic management department. In recent years, more and more experts and scholars have devoted themselves to applying sensors, network communication and dynamic adaptive technologies to road traffic management systems. At present, people have not completely overcome all kinds of complex problems in traffic supervision. The complexity of traffic information and the defects of identification algorithms have brought great challenges to intelligent traffic management. This article has launched a research on the intelligent Internet of Things traffic management system, with the detection and recognition of stationary vehicles and seat belts as the key analysis targets. When monitoring stationary vehicles, this paper replaces the background difference algorithm commonly used in dynamic vehicle detection with a new recognition algorithm. From the experimental results, the average detection accuracy of the new algorithm is 96.77% higher than the previous 87.56%. When studying the driver's seat belt detection, this paper combines the yolov3 target detection algorithm and the lightweight network structure, and proposes a driver-oriented positioning algorithm. With the increase in the number of lightweight templates, the accuracy of the positioning algorithm has increased from 80.57 to 99.98%. But on the other hand, the detection speed has also changed from 78 to 69 frames/s.

关键词： Intelligent Internet of Things Traffic management systems Stationary vehicle positioning algorithm Seat belt detection and recognition yolov3 algorithm

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