Abstract: With the continuous advancement of the transportation industry,the necessity of helmet-wearing during the riding of electric bicycles has been consistently verified.Meanwhile,the detection of helmet-wearing by electric bicycle riders has also received extensive research in the domain of deep learning.Currently,there exist issues such as significant detection difficulties in dense scenarios and challenges in detecting small targets when it comes to helmet-wearing by electric bicycle riders.Concurrently,in order to achieve more effective deployment on mobile terminals,a lightweight model needs to be selected.Hence,an improved detection algorithm for helmet-wearing by electric bicycle riders based on YOLOv8n has been proposed.Firstly,YOLOv8n is the most lightweight model among the YOLOv8 series,enabling better deployment on mobile devices.Additionally,a switchable atrous convolution is introduced into the backbone network of YOLOv8n,enhancing the feature extraction capability of YOLOv8n in dense scenes without increasing the computational load.At the end of the image pyramid feature fusion network of YOLOv8n,a triple attention mechanism is integrated to strengthen the extraction ability of important features from the fused information of different-scale features by the YOLOv8n model.Finally,the fusion of large-sized feature information with small-sized feature information is added to improve the detection effect of small targets.Ultimately,while ensuring the lightweight nature of YOLOv8n,when using the improved model in the self-constructed validation set,the mAP@50,mAP@50-90,and recall rate have increased by 2.7%,2.8% and 2.5%,respectively.Therefore,the proposed method holds certain practical and scientific significance.

Key words: Electric bicycle, Helemet detection, YOLOv8n, Empty convolution, Attention mechanis