In order to solve the problems of complex background，small target objects and difficult model deployment in UAV aerial images，a lightweight target detection algorithm for aerial images is proposed. The lightweight FasterNet module is introduced in the backbone network of YOLOv5m to replace C3 module，and the model parameters is compressed to improve reasoning speed of the model. In the feature fusion network，the improved CBAM_L mechanism is used to focus on capturing small target information in aerial images while improving the target recognition accuracy of the model. The detection head in the detection network is replaced by a decoupled head，which solves the conflict between classification and regression when outputting variables in aerial images; and the loss function in the network is replaced by EIoU，which effectively improves the model regression accuracy. The verification results on the public dataset VisDrone show that the average accuracy mAP@0.5 of the improved model is increased by 0.014，the parameter quantity and the computation cost is respectively reduced to 34.3% and 32.4% of the original model，and the detection speed reaches 77 frames per second. The results show that the proposed algorithm exhibits good performance in both detection accuracy and speed.