In response to the common problems of missed detections，false detections，and low detection accuracy in traditional anti-UAV detection methods，an improved YOLOv8 anti-UAV object detection algorithm，namely YOLO-DAP is proposed.Firstly，the large object detection layer (P5) is removed and the detection heads with size of 80×80，40×40 and 20×20 are replaced with new detection heads with size of 160×160，80×80 and 40×40，respectively，so as to improves the detection accuracy of small objects by the network. Secondly，DWR，an expandable residual attention module is introduced to improve Bottleneck block in C2f to improve the feature extraction ability of the network.Furthermore，ADown，a lightweight downsampling module，is introduced to better fuse feature maps of different scales.Finally，the PIoU loss function is used as the regression loss function，which makes the network have faster convergence speed and higher detection accuracy. Experiments on the public UAV data set TIB-Net show that the mAP of YOLO-DAP algorithm reaches 92.7%，which is 7.8 percentage points higher than that of the original YOLOv8n algorithm，and the number of parameters is reduced by 1.93×10⁶，which also has obvious advantages compared with the other mainstream object detection algorithms，and the effectiveness and advancement of the algorithm is proved.