Research on improved YOLOv5 electric vehicle helmet recognition algorithm

In order to determine whether electric vehicle drivers are wearing helmets, we propose a study of an improved YOLOv5 electric vehicle helmet recognition algorithm to detect whether an electric vehicle driver is wearing a helmet or not. First, by using the triple attention mechanism to capture interactions across dimensions in the backbone network, the target feature region can be localized more precisely. Then, by using the Focal-EIoU loss function to enhance the model training, the model's performance can be improved more effectively. Through experiments, it is shown that the enhanced YOLOv5 algorithm achieves a mean average precision (mAP) of 92.48%, which is 9.88% higher than the basic YOLOv5 algorithm. This accuracy has risen to 95.24%, meeting the requirements for recognizing the helmet-wearing condition of electric vehicle drivers on the road.