基于改进YOLOv5s的安全帽检测算法  

Helmet Detection Algorithm Based on Improved YOLOv5s

作　　者：邢雪凯 刘晨怡 胡国华 连顺 XING Xuekai;LIU Chengyi;HU Guohua;LIAN Shun(School of Advanced Manufacturing Engineering,Hefei University,Hefei 230601,China;iFLYTEK Co.,Ltd.,Hefei 230088,China)

机构地区：[1]合肥大学先进制造工程学院,合肥230601 [2]科大讯飞股份有限公司,合肥230088

出　　处：《西安文理学院学报(自然科学版)》2025年第1期21-26,共6页Journal of Xi’an University(Natural Science Edition)

基　　金：2022年度高等学校省级质量工程项目(2022sx128);2023年度安徽省新时代育人质量工程项目(研究生教育)(2023yjsxxsfkc046)

摘　　要：针对于工业场所中密集场景下安全帽佩戴出现的漏检情况以及提高检测精度,提出了一种基于改进YOLOv5s的工业安全帽检测算法.首先,采用CIOU优化Soft-NMS对密集人群重叠的情况,减少重叠目标的漏检,从而提高了安全帽检测的准确性.其次,在网络的中间层添加辅助训练头引入丰富的梯度信息,最后,辅助训练头结合Optimal Transport Assignment添加到Loss中,通过最优的目标匹配,减少模型的漏检和误检的情况,从而提升模型的准确率和召回率.实验结果表明,改进后的算法平均精确值(mAP@50-90)值为68.3%,相对于原YOLOv5s算法提升了3.8%,准确率为92.3%,相较于原YOLOv5s算法提高了0.7%.Aiming to the issue of missing detections of helmet wearing,a helmet detection algorithm based on improved version of YOLOv5s is proposed in order to enhance the accuracy of helmet detection in densely populated scenes within industrial environments.Firstly,CIOU is used to optimize Soft-NMS for handling overlapping instances,thereby reducing the occurrence of missed detections and improving the accuracy of helmet detection.Secondly,an auxiliary training head is introduced into the network's middle layer to incorporate rich gradient information.Finally,this auxiliary training head is integrated into Loss using Optimal Transport Assignment,which optimally matches targets to reduce cases of missing and false detections,enhancing both accuracy and recall rates of the model.Experimental results demonstrate that the improved algorithm achieves an average precision value(mAP@50-90)of 68.3%,surpassing the original YOLOv5s algorithm by 3.8%.Additionally,the accuracy of the improved algorithm reaches 92.3%,which is 0.7%higher than that achieved by the original YOLOv5s algorithm.

关 键 词：YOLOv5s 安全帽检测 Soft-NMS OTA 

分 类 号：TU714[建筑科学—建筑技术科学] TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]