基于YOLOv5s的施工人员安全防护设备佩戴检测研究  

Research on Construction Workers'Safety Protection Equipment Wearing Detection Based on YOLOv5s

作　　者：赵恩铭 杨松 罗创 刘光宇 朱晓亮[2] Zhao Enming;Yang Song;Luo Chuang;Liu Guangyu;Zhu Xiaoliang(College of Engineering,Dali University,Dali,Yunnan 671003,China;School of Information and Electronic Engineering,Zhejiang Gongshang University,Hangzhou 310018,China)

机构地区：[1]大理大学工程学院,云南大理671003 [2]浙江工商大学信息与电子工程学院,杭州310018

出　　处：《大理大学学报》2022年第12期37-42,共6页Journal of Dali University

基　　金：国家自然科学基金项目(62065001);云南省中青年学术和技术带头人后备人才项目(202205AC160001);教育部2020年产学合作协同育人项目(202002285009);云南省地方本科高校(部分)基础研究联合专项资金项目(2019FH001-066);浙江省自然科学基金基础公益研究计划项目(LGG20E040001)。

摘　　要：针对施工人员作业期间存在不规范佩戴安全帽、口罩的问题,采用YOLOv5s算法对施工人员进行安全防护设备佩戴检测,降低安全事故发生的概率。YOLOv5s算法与嵌入式平台Jetson Nano结合,使用TensorRT技术提升YOLOv5s算法的检测帧率,最终模型的mAP值为0.946,在Jetson Nano上的检测帧率为12.83 FPS。将YOLOv5s算法应用于施工现场安全保障领域,实现了对施工人员安全帽、口罩佩戴情况的有效监测,满足实际应用中检测速度与设备便携安装的需求。To solve the problem of irregular wearing of safety helmets and masks by construction workers'during operation,the YOLOv5s algorithm is used to detect the wearing of safety protection equipment for construction workers to reduce the probability of safety accidents.The YOLOv5s algorithm is combined with the embedded platform Jetson Nano,and the TensorRT technology is used to improve the detection frame rate of the YOLOv5s algorithm.The mAP value of the final model is 0.946,and the detection frame rate on the Jetson Nano is 12.83 FPS.The YOLOv5s algorithm is applied to the field of construction site safety assurance,which realizes effective monitoring of the wearing of construction workers'helmets and masks,and meets the needs of detection speed and portable installation of equipment in practical applications.

关 键 词：YOLOv5s 目标检测 TensorRT Jetson Nano 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]