软件导刊2024，Vol.23Issue(6)：143-149,7.DOI:10.11907/rjdk.231506
一种改进的YOLOv5电动车头盔佩戴检测方法
An Improved YOLOv5 Electric Vehicle Helmet Wearing Detection Method
摘要
Abstract
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.关键词
电动车头盔/YOLOv5/交通智能化/注意力机制/轻量化Key words
electric vehicle helmet/YOLOv5/traffic intelligence/attention mechanism/lightweight分类
计算机与自动化引用本文复制引用
刘超,高健..一种改进的YOLOv5电动车头盔佩戴检测方法[J].软件导刊,2024,23(6):143-149,7.基金项目
江苏省六大人才高峰项目(XXRJ-012) （XXRJ-012）
