基于CE-YOLOv5s的安全帽检测算法OACSTPCD

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.