In this paper, a lightweight YOLOv5 model (SG-YOLOv5) is proposed for the fast detection of the helmet and license plate of electric bikes, by introducing two mechanisms to improve the original YOLOv5.  To verify the performance of the SG-YOLOv5, the experiments are conducted on a homemade RHNP dataset, which contains four categories: rider, helmet, no-helmet, and license plate.  The results show that, the SG-YOLOv5 has the same mean average precision (mAP0.5) as the original; the number of model parameters, the FLOPs, and the model file size are reduced by 90.8%, 80.5%, and 88.8%, respectively.  Therefore, the proposed SG-YOLOv5 can effectively achieve the purpose of lightweight and improve the detection speed while maintaining great detection accuracy.
SG-YOLOv5