Improved electric bike helmet wearing detection algorithm for YOLOv5s

Abstract

Aiming at the problems of low detection accuracy and poor robustness of electric vehicle helmets in complex traffic scenarios, an improved YOLOv5-based electric vehicle helmet detection model is proposed. First, a shallow feature detection layer is added to enable the model to better detect small targets. Then, the attention mechanism is introduced in the backbone network, which optimizes the feature extraction ability of the network. Finally, replacing the CIoU loss function with WIoU improves the convergence speed and regression accuracy. The improved YOLOv5s algorithm was trained and tested on a homemade electric vehicle helmet dataset, and the improved YOLOv5s algorithm improved the average accuracy mean, accuracy rate, and recall rate by 6%, 4%, and 6.7%, respectively, over the original algorithm. The experimental results show that the electric vehicle helmet detection model with improved YOLOv5s algorithm has high helmet detection accuracy, excellent detection of small targets, and strong generalization capability.