An Improved YOLOv5 Electric Vehicle Helmet Wearing Detection Method
The use of deep learning algorithms to detect non motorized vehicle traffic violations can help accelerate the development of intelli-gent transportation in China and ensure traffic safety.To this end,design an automatic detection method for helmet wearing of electric bike rid-ers based on the improved YOLOv5 algorithm.This method is based on the YOLOv5 algorithm and utilizes Inception convolution to reduce the parameters of the feature extraction network,introducing an attention mechanism to optimize the object detection results.The experimental re-sults on the self built electric vehicle helmet dataset QCKJ-MH show that the average recognition accuracy of this method reaches 96.4%,the detection speed reaches 82 FPS,and the model size is 12.9 MB.It can accurately and quickly identify the wearing situation of electric vehicle riders' helmets.
electric vehicle helmetYOLOv5traffic intelligenceattention mechanismlightweight
国家科技期刊平台
NSTL
万方数据
