基于改进YOLOv5m的电动车骑行者头盔与车牌检测方法OACSTPCD

It has become a mandatory requirement for electric bike riders to wears helmet on riding.To automatically check if the electric bike rider wears helmet,a helmet and license plate detection approach based on improved YOLOv5m model is herein proposed,which can locate and recognize the license plate of the unhelmeted rider,so as to track down the violators.The model is trained with self-built dataset,uses DIOU loss function instead of GIOU loss function,and uses DIOU_NMS to replace weighted NMS so as to enhance the recognition ability for dense cycling scenes.Meanwhile,the ECA attention mechanism is added to the Backone and the Neck parts to im-prove the recognition accuracy for small-and medium-sized targets.Then,the K-means algorithm is used to re-cluster the anchor frame size.Finally,the Mosaic data enhancement method is improved.The experimental results show that the mAP of the proposed approach is 92.7%,which is 2.15,5.7,and 6.9 percentage points higher than the original YOLOv5m,YOLOv4 tiny,and Faster RCNN,respectively.It can be concluded that the improved YOLOv5m model can accurately recognize rider's helmet and electric bike's license plate.