基于YOLOv5的安全帽佩戴实时检测方法研究  

Research on Real-time Detection Method of Helmet Wearing Based on YOLOv5

作　　者：熊丽 王婷[1] XIONG Li;WANG Ting(of Civil and Architectural Engineering,Nanchang Hangkong University,Nanchang 330063,China)

机构地区：[1]南昌航空大学土木建筑学院,南昌330063

出　　处：《南昌航空大学学报（自然科学版）》2023年第1期93-100,共8页Journal of Nanchang Hangkong University(Natural Sciences)

基　　金：国家自然科学基金(51968051)。

摘　　要：安全帽作为施工现场工人必不可少的头部防护,佩戴安全帽对工人生命有着重要的意义。然而,由于缺乏安全意识,工人往往没有佩戴。随着深度学习技术的不断发展,具有很高精度和速度的YOLO系列算法已经被应用于各种场景检测任务中。为了建立数字化安全帽监控系统,本文提出了基于YOLOv5用于检测安全帽佩戴的方法,通过数据增强的样本扩充方法,使用基本图像并配合数据增强对数据集进行优化处理,自建一个特征丰富的安全帽佩戴数据集,从而使模型能够精确识别安全帽佩戴情况并达到实时检测的目的。实验结果表明,YOLOv5的平均检测速度达到60 f/s,达到实时检测的条件;mAP值达到98.5%,证明了基于YOLOv5的安全帽检测的有效性。As safety helmet provides essential head protection for workers at construction sites,wearing safety helmet is of great significance to their lives.However,workers often do not wear helmets due to a lack of safety awareness.With the continuous development of deep learning technology,YOLO series algorithms with high precision and speed have been applied to various scene detection tasks.In order to establish a digital helmet monitoring system,this paper proposes a method to detect helmet wearing based on YOLOv5.Through the sample expansion method of data enhancement,basic images and data enhancement are used to optimize the data set,and a self-developed data set with rich features about helmet wearing is built.Consequently,the model can accurately identify the wearing condition of helmet and realize the real-time detection.The experimental results show that the average detection speed of YOLOv5 reaches 60 f/s,which can meet the condition of real-time detection.The mAP value reaches 98.5%,which proves the effectiveness of the helmet detection based on YOLOv5.

关 键 词：YOLOv5 深度学习 安全帽检测 数据增强 实时检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]