Aiming at the problem of low recognition accuracy and instability of small target detection in traditional target detection algorithm, an improved small target detection algorithm based on YOLOv5 is proposed. Based on the convolutional neural network, an additional detector is added, the data enhancement strategy is adopted and the network convolution step is changed to solve the problems of low pixel, small proportion, easy overlap and difficult resolution of small targets. At the same time, relying on the real detection scene, a new satellite image data set for aircraft detection is produced, in which the proportion of small targets to be detected is 61%, and the aircraft attitude and scene are rich, which is helpful to verify the network accuracy objectively and comprehensively. Comparing the improved algorithm with the original YOLOv5 model, the results show that the average accuracy AP value of the improved algorithm is about 3% higher than that of the original YOLOv5 model.