Abstract:
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 detection 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 object detection neural network based on Bi-directional Feature Pyramid Network （BiFPN） is introduced， which performs both upward 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 convergence 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 accuracy 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.

Key words: Key words： helmet detection, YOLOv5, attentional mechanism, BiFPN, SIoU