Aiming at the problems of small target size and complex background in remote sensing vehicle detection tasks, a lightweight YOLOv5 algorithm based on multiple pyramids and multiscale attention is proposed. In the backbone network, the number of downsampling is reduced, the small target detection ability is improved, and light weight is achieved; in the neck, the information of different feature layers is fully utilized through the redesigned multi-pyramid network to enhance the feature fusion ability. And introduce an improved multi-scale attention module to obtain a larger receptive field and area of interest for the shallow feature map; finally, the K-means++ clustering algorithm is used to cluster and analyze the target size, and an anchor frame scale suitable for the target is designed. and aspect ratio. In the self-built remote sensing vehicle dataset, the target detection accuracy is not only improved, but also the parameter quantity is greatly reduced. Compared with YOLOv5s, AP0.5% is increased by 2.3%, AP0.5:0.75% is increased by 4.3%; the number of parameters is reduced by 65%, and the model size is reduced by 60%. It effectively improves the detection accuracy of small targets while reducing weight.