计算机与现代化Issue(4)：55-59,98,6.DOI:10.3969/j.issn.1006-2475.2024.04.010
基于CE-YOLOv5s的安全帽检测算法
Helmet Detection Algorithm Based on CE-YOLOv5s
摘要
Abstract
In the complex environment of construction sites,there are many dangerous factors,so the protection of the safety of workers has become a focus.Due to the chaotic environment and fixed information collection points at construction sites,there are problems of missed and false detection in safety helmet-wearing detection.Therefore,this paper proposes a safety helmet de-tection algorithm based on CE-YOLOv5s.The algorithm combines the SE attention mechanism with the C3 module,replaces the C3 module in the original network,assigns a higher weight to key features,and suppresses general features.Meanwhile,an ob-ject detection neural network based on Bi-directional Feature Pyramid Network(BiFPN)is introduced,which performs both up-ward and downward feature fusion,adds additional weights to each channel,and better preserves detailed information under low-resolution images.The SIoU loss function is introduced to improve the accuracy of boundary box positioning and accelerate con-vergence speed.Experimental results show that the improved network model has significantly improved in precision,recall,mAP@0.5,and mAP@0.5:0.95,effectively improving the detection accuracy of safety helmets and improving the detection accu-racy of small targets and obscured targets in cluttered backgrounds.When applied to construction sites,it can timely detect whether workers have taken protective measures,and better protect their safety.关键词
安全帽检测/YOLOv5/注意力机制/BiFPN/SIoUKey words
helmet detection/YOLOv5/attentional mechanism/BiFPN/SIoU分类
信息技术与安全科学引用本文复制引用
王志波,马晗,冯锦梁,刘国名..基于CE-YOLOv5s的安全帽检测算法[J].计算机与现代化,2024,(4):55-59,98,6.基金项目
国家自然科学基金资助项目(41872243) （41872243）
江西省教育厅青年科技基金资助项目(GJJ150572) （GJJ150572）
江西省教育厅科技计划一般项目(GJJ200721) （GJJ200721）
江西省放射性地球科学与大数据技术工程实验室开放基金资助项目(JELRGBDT201709) （JELRGBDT201709）
江西省网络空间安全智能感知重点实验室开放基金资助项目(JKLCIP202211) （JKLCIP202211）
江西省教育厅科技项目(GJJ200721) （GJJ200721）
