Research on Cycling Helmet Wearing Detection Algorithm Based on Improved YOLOv5s
E-bikes have gradually become a mainstream mode of transport due to their convenience.However,the casualty rate of e-bike riders in traffic accidents remains high.To solve the problem of the high casualty rate of e-bikes,the modified YOLOv5s is used to detect the wearing of helmets in road scenarios.Firstly,the GSConv module is introduced to replace the standard convolution in the original YOLOv5s backbone network,which improves the network speed while guaranteeing detection accuracy.Secondly,the CA(Coordinate Attention)mechanism is introduced to supplement the position information and improve the feature expression of the key information.Finally,the DIoU loss function is used to replace the GIoU loss function in the original YOLOv5s,improving the ability of target detection by the algorithm.The experimental results show that on the self-constructed dataset of e-bike riding helmets,the average accuracy of e-bike riding helmet detection by the modified YOLOv5s network improves by 3.7％compared to the original YOLOv5s,which indicates that the modified YOLOv5s network can realize the task of detecting the wearing of cycling helmets.
helmet detectionYOLOv5sGSConvattention mechanismDIoU loss function
万方数据
维普
