%0 Journal Article
%T 基于YOLOv5的矿工安全帽佩戴及自救器携带检测研究<br>Research on Miners’ Helmet Wearing and Self-Rescuer Carrying Detection Based on YOLOv5
%A 徐明智
%A 幸贞雄
%A 武熠明
%A 徐翔
%A 蔡永成
%J Journal of Security and Safety Technology
%P 41-45
%@ 2330-4685
%D 2023
%I Hans Publishing
%R 10.12677/JSST.2023.114005
%X 为增强矿山工作人员佩戴安全帽和携带自救器的意识，有效预防生产安全事故发生，基于Pytorch框架和YOLOv5目标检测算法，实现了对安全帽和自救器的自动检测。结果显示：模型对安全帽和自救器有较高的检测精度，mAP达到84.1%。使用矿山企业现场的监控录像对模型进行测试，能够准确检测到视频中的安全帽和自救器。<br />
To enhance the awareness of mine workers to wear safety helmets and carry self-rescuers, and effectively prevent production safety accidents, automatic detection of safety helmets and self- rescuers was realized based on the Pytorch framework and YOLOv5 target detection algorithm. The results show that the model has high detection accuracy for safety helmets and self-rescuers, and the mAP reaches 84.1%. The model was tested using surveillance video from the site of a mining enterprise and was able to accurately detect helmets and self-rescuers in the video.
%K 安全管理，安全帽检测，自救器检测，计算机视觉，YOLOv5<br>Safety Management
%K Helmet Detection
%K Self-Rescuer Detection
%K Computer Vision
%K YOLOv5
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=76513