For small target detection in aerial images of UAVs, due to limitations such as low pixel values, lack of rich features, difficulty in feature extraction, and susceptibility to environmental interference, it is easy to lead to missed detections, low accuracy, and excessive network parameter quantities. To solve the problems, a small target detection model for UAVs based on improved YOLOv5s network is proposed. The YOLOv5s network structure is improved by reducing the network structure and parameter quantity without adding small object detection heads, making the model lighter. A pyramid pooling module, ASPPF, is proposed, which adds dilated convolution to the SPPF module of the YOLOv5s network to enhance the spatial invariance of feature information and enhance the spatial invariance of feature information. The perception ability of the network towards small targets is improved by adopting a Cross Layer Upsampling (CLAU) attention module. After the upsampling process, the low-resolution deep features are fused with the high-resolution shallow features to improve the detection efficiency of small target images. The EIoU loss function is used to replace the original CIoU loss function to improve the convergence speed of training. Validation on the VisDrone2019 dataset shows that: 1) The improved model performs well in mAP@0.5 and mAP@0.5∶0.95 with values of 41.2% and 23.4% respectively, which are 7.2 and 4.7 percentage points higher than that of the original model; and 2) The number of parameters is only 49% of the original model.