Electiric Bike Helment Wearing Detection Alogrithm Based on Improved YOLOv5

In electric vehicle traffic accidents, craniocerebral injury is the main cause of death of electric vehicle riders, and most electric vehicle riders rarely wear helmets.Therefore, it is of strong practical significance to supervise the helmet wearing situation of electric vehicle riders by combining the target detection algorithm with road cameras.For the current problems of electric vehicle helmet wearing detection: the high leakage rate of targets blocking each other, and the high leakage rate of smaller targets, this paper proposes an improved YOLOv5 target detection algorithm to achieve the detection of electric vehicle helmet wearing.The method first adds the channel attention mechanism ECA-Net to the YOLOv5 network, so that the model can detect the target features, thus improving the model detection performance; the Bi-FPN weighted bidirectional feature pyramid module is used to achieve a balance of the importance of features at different levels, which is conducive to improving the small target miss det