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.