For the electric vehicle helmet wearing detection method exists in complex road conditions helmet small target detection accuracy is low, the target mutual occlusion leakage rate is high, detection model large arithmetic complexity and other problems. This question proposes a target detection algorithm based on improved YOLOv10n to solve these problems in practical applications. Firstly, the advantages of BiFPN are integrated on the basis of MAFPN, and the BIMAFPN structure is innovatively proposed, which improves the detection performance of the model in complex road scenarios. Secondly, the Inner-Wise-MPDIoU loss function is constructed to replace the traditional CIoU loss function, which improves the detection accuracy of the network while accelerating the convergence process of the model. Finally, the LSCD detection head is introduced to further reduce the number of model parameters and improve the detection performance. Experimental results show that compared with the original model, the improved model improves the accuracy of mAP@0.5 by 2.7%, the number of parameters is reduced by 25%, and the model size is reduced by 35%. The detection method used in this paper not only significantly improves the accuracy of helmet detection under complex road conditions, but also maintains good real-time performance while taking into account lightweight, which makes it easy to deploy the model in small embedded transport devices.