A two-step algorithm for detecting helmet wear in electric vehicles based on YOLOv5-FD

To address the issue of detecting electric vehicle helmet wearing in complex environments where pedestrians and electric vehicles are moving in the same direction, we propose an improved algorithm based on YOLOv5s. To guarantee a lightweight model that enhances accuracy, we replaced the backbone structure of YOLO with FasterNet-T1 and introduced the DSConv module in the neck network of YOLO to reduce model computation without sacrificing accuracy. The experimental results demonstrate that the improved algorithm enhances the mean average precision (mAP) by 3.1% and reduces computation by 7.3% compared to the YOLOv5s algorithm. This improvement ensures higher detection accuracy while reducing computation, making it valuable for certain applications. Additionally, the improved model enhances the generalisation of detection compared to other mainstream detection models, making it applicable to a wider range of detection scenarios.