To solve the problems of serious missed detection and false detection caused by the large number of small target samples with a wide scale range in UAV aerial images，an improved small target detection algorithm named SD-YOLO is proposed based on YOLOv8s.Firstly，the C2f is reconstructed by using DCNv2 module，so that the model can effectively capture the fine-grained details of the target and adaptively adjust the sampling position of the convolution kernel，so as to accurately perform target positioning.Secondly，the SPD-Conv module is improved to enhance the model's ability of capturing local features，so that the model can retain more spatial information.Finally，a small target detection head is added，and the Dynamic head module is introduced to improve detection performance of the model in multi-scale scenes.Experimental results on VisDrone2019 dataset show that SD-YOLO has an mAP50 of 0.495，which is 0.1 higher than that of the original YOLOv8s network，and it is able to maintain a high frame rate，which significantly improves the detection performance of multi-scale small targets.