In recent years, traffic accidents caused by electric vehicle drivers driving without helmets have occurred frequently, resulting in great personal injury and loss. The investigation shows that most accidents occur at traffic intersections. Therefore, it is necessary to carry out the monitoring and control of helmet wearing behavior of electric vehicle drivers at traffic intersections. In this paper, a large number of target data of electric vehicles and drivers are collected by machine vision sensors, and the corresponding data sets are made. The processed data sets are trained on the pytoch framework by using the improved yolov5 neural network to obtain the optimal weight parameters; Compared with the original neural network, the improved Yolov5 algorithm has a detection accuracy of 92% and 98% for electric vehicles and helmets, which is 1% to 2% higher than that of the original neural network. Finally, the training improved yolov5 model and sort algorithm are used together to track and label electric vehicles while detecting their wearing helmets, so as to realize the effective control of illegal electric vehicle driving behavior at traffic intersections.