Aiming at the UAV aerial photography viewpoint target detection spatial scale change is large, the object pixels account for a small proportion, and the algorithm deployment edge computing platform storage space occupies a large proportion of the problem. In this paper, based on the YOLOv8n network structure, an improved aerial photography viewpoint lightweight small target detection method DSF-YOLO-P algorithm is proposed. Firstly, the backbone network C2f module is integrated with FasterNet to form the Faster-C2f lightweight module to ensure that the model achieves network lightweighting and improves the detection speed without affecting the detection accuracy. Then, a new 160×160 prediction head is added and the network channels are reconfigured to improve the accuracy and robustness of the model for small target detection. The improved DSF-YOLO algorithm improves the accuracy by 2.5% and 0.6% on the visible dataset VisDrone2019 and infrared dataset HIT-UAV, respectively, and reduces the number of parameters by 10%. Finally, the DSF-YOLO algorithm is subjected to the dependency graph pruning operation to reduce the redundant parameters of the model without affecting the model performance. The pruned DSF-YOLO-P algorithm achieves the same accuracy and reduces the computational effort and number of parameters by 45% and 26%, respectively, compared with the DSF-YOLO algorithm on the VisDrone2019 dataset. The experimental results fully demonstrate the effectiveness of the DSF-YOLO-P algorithm in detecting small targets in the aerial view of UAVs.