Aiming at the problems of long shooting distance, small target, high density and mutual occlusion of objects in aerial images of Unmanned Aerial Vehicles(UAVs), an improved YOLOv8s algorithm called DD-YOLO is introduced, which combines deformable depth-wise convolution and multiple attention mechanisms. The algorithm integrates depth-wise convolution to simplify the network model, and proposes deformable depth-wise convolution to optimize C2f module and enhance the feature extraction ability of backbone network. The SE and MHSA attention mechanisms are introduced to transform the structure of SPPF to make it take into account the extraction of local and global features. Quadruple downsampling branches are added to the neck network to alleviate the lack of receptive field for small targets, optimize target localization, and strengthen the focus on small targets. Experiments show that the improved model achieves an mAP@50 of 43. 9% and a mAP@50∶95 of 26. 7% on the VisDrone-DET2019 dataset, which is 5. 1 and 3. 6 percentage points higher than that of YOLOv8s, respectively, with a 13. 2% reduction in parameter quantity and 12. 6% reduction in model size which is of great significance for the realization of UAV small target detection.