基于改进YOLOv5的电动车头盔佩戴检测算法  

Electiric Bike Helment Wearing Detection Alogrithm Based on Improved YOLOv5

作　　者：谢溥轩 崔金荣[1,2] 赵敏 XIE Puxuan;CUI Jinrong;ZHAO Min(College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China;Guangzhou Key Laboratory of Intelligent Agriculture,Guangzhou 510642,China;Shenzhen Institute of Artificial Intelligence and Robotics,Shenzhen,Guangdong 518129,China)

机构地区：[1]华南农业大学数学与信息学院,广州510642 [2]广州市智慧农业重点实验室,广州510642 [3]深圳市人工智能与机器人研究院,广东深圳518129

出　　处：《计算机科学》2023年第S01期410-415,共6页Computer Science

基　　金：国家重点研发计划(2019YFB1310402);国家自然科学基金面上项目(1,62172285)。

摘　　要：在电动车交通事故中,颅脑损伤致死是电动车骑行人员死亡的主要原因,而大多数电动车骑行人员很少佩戴头盔,因此通过将目标检测算法与道路摄像头结合来监管电动车骑行者头盔佩戴情况具有很强的现实意义。针对目前电动车头盔佩戴检测存在着目标相互遮挡漏检率较高、较小目标漏检率较高的问题,文中提出了一种改进的YOLOv5目标检测算法,用于实现对电动车头盔佩戴情况的检测。该方法首先在YOLOv5网络中添加通道注意力机制ECA-Net,使得模型能够更快地检测到目标特征,从而提高模型的检测性能;其次,使用Bi-FPN加权双向特征金字塔模块,实现对不同层级特征重要性的平衡,有利于改进小目标漏检问题;最后,使用Alpha-CIoU Loss的损失函数,提高模型定位的准确性。实验结果表明,该方法在3种场景下对电动车骑行人员头盔佩戴情况的检测精度均高于其他模型,平均精度达到95.8%,相比原网络检测精度有所提升,实现了电动车头盔佩戴情况的高精度检测。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 to achieve 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.

关 键 词：深度学习 头盔佩戴检测 YOLOv5 Bi-FPN ECA-Net Alpha-CIoU 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]