An improved YOLO v5 model for cyclist helmet and license plate detection is proposed to solve the problems of low accuracy,poor generalization ability and single detection categories in helmet detection.Firstly,the convolutional block attention module (CBAM) is introduced into the backbone network to strengthen the key features of the target region and improve the accuracy of the model. Secondly,by optimizing the multi-scale feature module and adding a detection layer for tiny targets in the prediction end,the detection rate of the network for small targets in dense scenes is enhanced,and the generalization ability of the model is improved.Finally,the model training convergence speed is accelerated and target localization accuracy is improved by optimizing the bounding box regression using efficient intersection over union (EIoU) and by clustering new anchor box sizes using the K-means algorithm in the helmet and license plate dataset created.The experimental results show that the improved YOLO v5 model has achieved an increase in detection accuracy rate of 2.5%,a recall rate increase of 3.3%,and an average precision increase of 3.8%,which makes it more suitable for detecting helmet and license plate targets of cyclists.