基于YOLO v5的安全帽佩戴检测研究
Safety Helmet Wearing Detection Based on YOLOv5
李会民 1付世勋 1申炜涛 1张立德1
作者信息
- 1. 北华航天工业学院计算机学院,河北廊坊 065000
- 折叠
摘要
对于建筑业、钢铁制造业、煤矿行业等高危行业的工人来说,做好安全防护是重中之重.佩戴安全帽是避免受伤的有效途径之一.为更加准确、精准地检测工人安全帽的佩戴情况,提出一种基于YOLOv5的安全帽检测方法研究,来对安全帽的佩戴情况进行检测,提高安全帽检测模型的准确率,可以减少出现一些检测错误或者检测精度不够的情况出现,从而可以更有效地避免一些安全事故的发生.
Abstract
For workers in high-level risk industries such as construction,steel manufacturing,and coal mining,safety protection is a top priority,and wearing a safety helmet is also one of the effective ways to avoid injury.However,in order to detect the wearing of workers'safety helmets more accurately,a safety helmet detection method based on YOLOv5 is proposed to improve the accuracy of safety helmet detection models,reduce some detection errors or insufficient detection accuracy,and thus avoid some safety accidents more effectively.
关键词
深度学习/安全帽佩戴检测/YOLO/v5Key words
deep learning/helmet detection/YOLOv5引用本文复制引用
出版年
2024
NETL
NSTL
万方数据