Aiming at the problems of low accuracy, missing detection and false detection in the identification of small targets in UAV aerial images by existing target detection methods, a small target detection method based on improved YOLOv7 is proposed. Firstly, the network detection head is improved, the 20×20 detection head originally used for detecting large objects is removed, a 160×160 detection head for small targets is added, and the network feature fusion path is modified accordingly. Secondly, the SPPCSPC structure is reconstructed, the convolutional layer is clipped and the pooling structure is changed to reduce module complexity and speed up network convergence. Then, the original upsampling structure is replaced by the Content-Aware ReAssembly of FEatures (CARAFE) operator to reduce the loss of image information during upsampling and maximize the preservation of local and corner information of the input image. Finally, the ELAN module is improved to lighten the backbone while improving the sensitivity of the network to small-scale targets. Experiments are carried out on the public dataset VisDrone2019, and the mAP50 of the improved model reached 56.6%, which is 2.9 percentage points higher than that of the original YOLOv7 model, and the parameters are reduced by 33%.