首页|Traffic safety helmet wear detection based on improved YOLOv5 network
Traffic safety helmet wear detection based on improved YOLOv5 network
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Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model,a method based on improving you only look once version 5(YOLOv5)is proposed.By incorporating the lightweight GhostNet module into the YOLOv5 backbone network,we effectively reduce the model size.The addition of the receptive fields block(RFB)module enhances fea-ture extraction and improves the feature acquisition capability of the lightweight model.Subsequently,the high-performance lightweight convolution,GSConv,is integrated into the neck structure for further model size com-pression.Moreover,the baseline model's loss function is substituted with efficient insertion over union(EIoU),accel-erating network convergence and enhancing detection precision.Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.
GUI Dongdong、SUN Bo
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College of Software,Southeast University,Suzhou 215123,China
Quanzhou Equipment Research Centre,Haixi Institute,Chinese Academy of Sciences,Quanzhou 350108,China
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