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Special Vehicle Target Detection and Tracking Based on Virtual Simulation Environment and yolov5-Block+DeepSort Algorithm

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Computers, Materials & Continua 2024年第11期81卷 3241-3260页

作者： Mingyuan Zhai Hanquan Zhang Le Wang Dong Xiao Zhengmin Gu Zhenni Li College of Information Science and Engineering Northeastern UniversityShenyang110819China Flight Control Department Shenyang Aircraft Design and Research InstituteShenyang110035China Department of Information Center The First Hospital of China Medical UniversityShenyang110001China

In the process of dense vehicles traveling fast,there will be mutual occlusion between vehicles,which will lead to the problem of deterioration of the tracking effect of different vehicles,so this paper proposes a research method of virtual simulation video vehicle target tracking based on you only look once(YOLO)v5s and deep simple online and realtime tracking(DeepSort).Given that the DeepSort algorithm is currently the most effective tracking method,this paper merges the yolov5 algorithm with the DeepSort *** it adds the efficient channel attention networks(ECA-Net)focusing mechanism at the back for the cross-stage partial bottleneck with 3 convolutions(C3)modules about the yolov5 backbone network and before the up-sampling of the Neck feature *** yolov5 algorithm adopts expected intersection over union(EIOU)instead of complete intersection over union(CIOU)as the loss function of the target frame *** improved yolov5 algorithm is named *** experimental results show that in the special vehicle target detection(TD)and tracking in the virtual simulation environment,The yolov5-Block algorithm has an average accuracy(AP)of 99.5%,which significantly improves the target recognition correctness for typical occlusion cases,and is 1.48 times better than the baseline *** the virtual simulation video sequence test,multiple objects tracking accuracy(MOTA)and various objects tracking precision(MOTP)improved by 10.7 and 1.75 percentage points,respectively,and the number of vehicle target identity document(ID)switches *** with recent mainstream vehicle detection and tracking models,the yolov5-Block+Deepsort algorithm can accurately and continuously complete the detection and tracking tasks of special vehicle targets in different scenes.

关键词： yolov5 DeepSort virtual simulation attention mechanism

C2Net-yolov5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm

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Computers, Materials & Continua 2023年第11期77卷 1949-1965页

作者： Xiujuan Wang Yiqi Tian Kangfeng Zheng Chutong Liu Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100048China Fan Gongxiu Honors College Beijing University of TechnologyBeijing100124China

Rapid advancement of intelligent transportation systems(ITS)and autonomous driving(AD)have shown the importance of accurate and efficient detection of traffic ***,certain drawbacks,such as balancing accuracy and real-time performance,hinder the deployment of traffic sign detection algorithms in ITS and AD *** this study,a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and *** enhanced backbone network module,called C2Net,which uses an upgraded bidirectional Res2Net,was introduced to mitigate information loss in the feature extraction process and to achieve information ***,a squeeze-and-excitation attention mechanism was incorporated within the channel attention of the architecture to perform channel-level feature correction on the input feature map,which effectively retains valuable features while removing non-essential features.A series of ablation experiments were conducted to validate the efficacy of the proposed *** performance was evaluated using two distinct datasets:the Tsinghua-Tencent 100K and the CSUST Chinese traffic sign detection benchmark *** the TT100K dataset,the method achieves precision,recall,and Map0.5 scores of 83.3%,79.3%,and 84.2%,***,on the CCTSDB 2021 dataset,the method achieves precision,recall,and Map0.5 scores of 91.49%,73.79%,and 81.03%,*** results revealed that the proposed method had superior performance compared to conventional models,which includes the faster region-based convolutional neural network,single shot multibox detector,and you only look once version 5.

关键词： Target detection traffic sign detection autonomous driving yolov5

A Transmission and Transformation Fault Detection Algorithm Based on Improved yolov5

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Computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a yolov5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line yolov5 multi-scale integration SENet

Zero-DCE++Inspired Object Detection in Less Illuminated Environment Using Improved yolov5

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Computers, Materials & Continua 2023年第12期77卷 2751-2769页

作者： Ananthakrishnan Balasundaram Anshuman Mohanty Ayesha Shaik Krishnadoss Pradeep Kedalu Poornachary Vijayakumar Muthu Subash Kavitha Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Information and Data Sciences Nagasaki UniversityNagasaki8528521Japan

Automated object detection has received the most attention over the *** cases ranging from autonomous driving applications to military surveillance systems,require robust detection of objects in different illumination ***-of-the-art object detectors tend to fare well in object detection during daytime ***,their performance is severely hampered in night light conditions due to poor *** address this challenge,the manuscript proposes an improved yolov5-based object detection framework for effective detection in unevenly illuminated nighttime ***,the preprocessing strategies involve using the Zero-DCE++approach to enhance lowlight *** is followed by optimizing the existing yolov5 architecture by integrating the Convolutional Block Attention Module(CBAM)in the backbone network to boost model learning capability and Depthwise Convolutional module(DWConv)in the neck network for efficient compression of network *** Night Object Detection(NOD)and Exclusively Dark(ExDARK)dataset has been used for this *** proposed framework detects classes like humans,bicycles,and *** demonstrate that the proposed architecture achieved a higher Mean Average Precision(mAP)along with a reduction in model size and total parameters,*** proposed model is lighter by 11.24%in terms of model size and 12.38%in terms of parameters when compared to baseline yolov5.

关键词： Object detection deep learning nighttime road scenes yolov5 DWConv Zero-DCE++ CBAM

Real-time Safety Helmet-wearing Detection Based on Improved yolov5

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Computer Systems Science & Engineering 2022年第12期43卷 1219-1230页

作者： Yanman Li Jun Zhang Yang Hu Yingnan Zhao Yi Cao NARI Nanjing Control System Ltd. Nanjing210032China School of Computer Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorN9B 3P4Canada

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the yolov5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the yolov5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

关键词： Safety helmet wearing detection object detection deep learning yolov5 Attention Mechanism

GL-yolov5: An Improved Lightweight Non-Dimensional Attention Algorithm Based on yolov5

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Computers, Materials & Continua 2024年第11期81卷 3281-3299页

作者： Yuefan Liu Ducheng Zhang Chen Guo School of Software Dalian Jiaotong UniversityDalian116000China

Craniocerebral injuries represent the primary cause of fatalities among riders involved in two-wheeler accidents;nevertheless,the prevalence of helmet usage among these riders remains alarmingly ***,the accurate identification of riders who are wearing safety helmets is of paramount *** detection algorithms exhibit several limitations,including inadequate accuracy,substantial model size,and suboptimal performance in complex environments with small *** address these challenges,we propose a novel lightweight detection algorithm,termed GL-yolov5,which is an enhancement of the You Only Look Once version 5(yolov5)*** model incorporates a Global DualPooling NoReduction Blend Attention(GDPB)module,which optimizes the MobileNetV3 architecture by reducing the number of channels by half and implementing a parallelized channel and spatial attention mechanism without dimensionality ***,it replaces the conventional convolutional layer with a channel shuffle approach to overcome the constraints associated with the Squeeze-and-Excitation(SE)attention module,thereby significantly improving both the efficiency and accuracy of feature extraction and decreasing computational ***,we have optimized the Variable Normalization and Attention Channel Spatial Partitioning(VNACSP)within the C3 module of yolov5,which enhances sensitivity to small targets through the application of a lightweight channel attention mechanism,substituting it for the standard convolution in the necking *** Parameter-Free Spatial Adaptive Feature Fusion(PSAFF)module is designed to adaptively modify the weights of each spatial position through spatial pooling and activation functions,thereby effectively enhancing the model’s ability to perceive contextual information over ***,GL-yolov5 performs remarkably in the custom dataset,achieving a model parameter count of 922,895 M,a computational load of 2.9 GFLOPS,an

关键词： Lightweight traffic safety helmet detection yolov5 GDPB PSAFF

An Improved Distraction Behavior Detection Algorithm Based on yolov5

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Computers, Materials & Continua 2024年第11期81卷 2571-2585页

作者： Keke Zhou Guoqiang Zheng Huihui Zhai Xiangshuai Lv Weizhen Zhang College of Information Engineering Henan University of Science and TechnologyLuoyang471023China

Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance *** the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of ***,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different *** the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on *** algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention *** emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex ***,a Triple Feature Encoding(TFE)module has been added to the neck *** module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the ***,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box *** evaluation of the improved yolov5 distraction detection algorithm against the original yolov5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.

关键词： Distracted driving yolov5 triple feature encoding shape-IoU

Fire Detection Algorithm Based on an Improved Strategy of yolov5 and Flame Threshold Segmentation

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Computers, Materials & Continua 2023年第6期75卷 5639-5657页

作者： Zhao Yuchen Wu Shulei Wang Yaoru Chen Huandong Zhang Xianyao Zhao Hongwei School of Information Science and Technology Hainan Normal UniversityHaikou571158China

Due to the rapid growth and spread of fire,it poses a major threat to human life and *** use of fire detection technology can reduce disaster *** traditional threshold segmentation method is unstable,and the flame recognition methods of deep learning require a large amount of labeled data for *** order to solve these problems,this paper proposes a new method combining You Only Look Once version 5(yolov5)network model and improved flame segmentation *** the basis of the traditional color space threshold segmentation method,the original segmentation threshold is replaced by the proportion threshold,and the characteristic information of the flame is maximally *** the yolov5 network model,the training module is set by combining the ideas of Bootstrapping and cross validation,and the data distribution of yolov5 network training is *** the same time,the feature information after segmentation is added to the data *** from the training method that uses large-scale data sets for model training,the proposed method trains the model on the basis of a small data set,and achieves better model detection results,and the detection accuracy of the model in the validation set reaches *** results show that the proposed method can detect flame features with faster speed and higher accuracy compared with the original method.

关键词： yolov5 fire safety deep learning flame detection

Fine Grained Feature Extraction Model of Riot-related Images Based on yolov5

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Computer Systems Science & Engineering 2023年第4期45卷 85-97页

作者： Shaofan Su Deyu Yuan Yuanxin Wang Meng Ding Department of Information Cyber Security People’s Public Security University of ChinaBeijing100038China Key Laboratory of Safety Precautions and Risk Assessment Ministry of Public SecurityBeijing102623China Public Security Behavioral Science Laboratory People’s Public Security University of ChinaBeijing102623China

With the rapid development of Internet technology,the type of information in the Internet is extremely complex,and a large number of riot contents containing bloody,violent and riotous components have *** contents pose a great threat to the network ecology and national *** a result,the importance of monitoring riotous Internet activity cannot be *** Neural Network(CNN-based)target detection algorithm has great potential in identifying rioters,so this paper focused on the use of improved backbone and optimization function of You Only Look Once v5(yolov5),and further optimization of hyperparameters using genetic algorithm to achieve fine-grained recognition of riot image ***,the fine-grained features of riot-related images were identified,and then the dataset was constructed by manual ***,the training and testing work was carried out on the constructed dedicated dataset by supervised deep learning *** research results have shown that the improved yolov5 network significantly improved the fine-grained feature extraction capability of riot-related images compared with the original yolov5 network structure,and the mean average precision(mAP)value was improved to ***,it provided strong support for combating riot-related organizations and maintaining the online ecological environment.

关键词： Convolutional neural network yolov5 riot-related fine grained target detection