A Helmet Wear Detection and Tracking Method Based on YOLOv8 and Bytetrack

In this paper, we propose a novel helmet detection and tracking algorithm named PG-YOLOv8-byte, which builds on the YOLOv8 framework. This enhanced algorithm incorporates the C2f-PConv module in place of the original C2F, reducing computational demands and thereby accelerating model inference. Additionally, we have introduced a GAM attention mechanism to improve the precision in identifying the region of interest within targets. To address the detection of small objects, a specialized detection head has been added, enhancing accuracy for tiny targets. For multi-target tracking, we utilize the Bytetrack model, facilitating effective helmet detection and tracking in construction environments.