To solve the problems of a large number of small target samples and inadequate extractable feature information in UAV aerial photography images,a small target detection algorithm based on the improved YOLOv7 is proposed.Firstly,the low-level small target detection layer in the backbone network is integrated into the aggregation network structure,and a header is added to detect extremely small targets.Secondly,channel-spatial attention modules are added to the feature extraction process of the backbone network.At the same time,the feature fusion mode of improving the original connection in feature fusion is introduced,and the output weight of the feature graphs of each level is generated adaptively to dynamically optimize the representation ability of the feature graphs.Finally,the positioning loss function of SIoU is introduced into the prediction process to improve the model‘s detection ability and positioning accuracy.Experimental results show that the mAP50 of the improved model reaches 52.6%,which is 2.8 percentage points higher than that of the baseline YOLOv7 algorithm.The improved model also achieves higher detection accuracy than the mainstream detection methods,and has better performance in small target detection.