Abstract: A modified detection algorithm of electric bicycle helmet based on YOLOv5s(You Only Look Once version 5 small) is proposed to address the issues of small target missed detection and low accuracy in electric bicycle helmet wearing detection. CBAM ( Convolutional Block Attention Module) is introduced into the backbone network enhancing attention to clustered targets and effectively solving the problem of poor detection performance caused by occlusion. The PANet structure in the neck network is changed to a feature fusion structure that combines the idea of cross-scale feature fusion network (BiFPN: Bidirectional Feature Pyramid Network) enhances the multi-scale fusion ability of the model in different directions and effectively fuses multi- scale features of the target. Using SIoU(Structured Intersection over Union)localization loss function instead of CIoU(Complete Intersection over Union)loss function improves the accuracy of bounding box regression. The experimental results show that the accuracy P and recall R of the improved YOLOv5s model are 94. 7% and 91. 2%, respectively, and the average accuracy value mAP is 95. 6%, which is 6%,7%, and 6. 5% higher than that of the original YOLOv5s model, respectively. The method has significantly improved the accuracy of electric bicycle helmet wearing detection.

Key words: electric bicycle helmet, YOLOv5s, object detection, convolutional block attention module (CBAM)attention mechanism, bidirectional feature pyramid network (BiFPN)