Abstract: In electric vehicle traffic accidents,craniocerebral injury is the main cause of death of electric vehicle riders,and most electric vehicle riders rarely wear helmets.Therefore,it is of strong practical significance to supervise the helmet wearing situation of electric vehicle riders by combining the target detection algorithm with road cameras.For the current problems of electric vehicle helmet wearing detection:the high leakage rate of targets blocking each other,and the high leakage rate of smaller targets,this paper proposes an improved YOLOv5 target detection algorithm to achieve the detection of electric vehicle helmet wearing.The method first adds the channel attention mechanism ECA-Net to the YOLOv5 network,so that the model can detect the target features,thus improving the model detection performance;the Bi-FPN weighted bidirectional feature pyramid module is used toachieve a balance of the importance of features at different levels,which is conducive to improving the small target miss detection problem;the loss function of Alpha-CIoU Loss is used to improve the accuracy of model localization.Experimental results show that the detection accuracy of the method is higher than other models for the helmet wearing situation of electric vehicle riders in all three scenarios,with an average accuracy of 95.8%,which is higher than the original network detection accuracy,and achieves high accuracy detection of electric vehicle helmet wearing situation.

Key words: Deep learning, Helmet wearing detection, YOLOv5, Bi-FPN, ECA-Net, Alpha-CIoU