基于轻量化YOLOv5的安全帽检测  

Safety Helmet Detection Based on Lightweight YOLOv5

作　　者：李延满[1] 王必恒[1] 赵羚焱 LI Yan-man;WANG Bi-heng;ZHAO Ling-yan(Nari Technology Co.,Ltd.,Nanjing 210000,China;School of Computer Science,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]国电南瑞科技股份有限公司,江苏南京210000 [2]南京信息工程大学计算机学院,江苏南京210044

出　　处：《计算机与现代化》2023年第10期59-64,共6页Computer and Modernization

基　　金：国家自然科学基金资助项目(61601235);国电南瑞科技股份有限公司项目(2022h275)。

摘　　要：配电网运维施工安全智能监控系统中存在大量数据,客观上要求算法具有较高实时性。基于此,本文轻量化改进YOLOv5算法,具体包括改进K-means算法聚类锚框,采用Hard-swish激活函数和CRD损失函数,同时在主干网融合ShuffleNet结构以及FPN模块增加Attention机制。该模型SNAM-YOLOv5 (ShuffleNet and Attention Mechanism-You Only Look Once version 5)能够显著提高小目标和遮挡目标的检测性能以及处理速度。在基于海思Hi3559A嵌入式平台进行安全帽检测的运行结果表明,该模型优于同类算法,同时具有良好的实时性。There is a large amount of data in the intelligent monitoring system of distribution network,which objectively requires the algorithm to have high real-time performance.Based on this,the YOLOv5 algorithm is improved in light weight,including improving the K-means algorithm clustering anchor box,using the Hard-swish activation function and the CRD loss function,and at the same time integrating the ShuffleNet structure in the backbone network and adopting the Attention mechanism in the FPN module.The presented model,SNAM-YOLOv5(ShuffleNet and Attention Mechanism-You Only Look Once version 5),can significantly improve the detection performance and the processing speed of small targets and occluded targets.The results of safety helmet detection based on HiSilicon Hi3559A embedded platform show that the model is superior to similar algorithms and has good real-time performance.

关 键 词：深度学习 配电网运维 施工安全 智能监控 轻量化网络 安全帽检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]