UAV aerial photography is one of the mainstream object detection technologies, and this task faces problems such as small target objects, large scale changes, and complex background interference. How to improve the detection accuracy with limited computing resources is an important challenge. In order to solve the above problems, a lightweight UAV aerial target detection method was proposed. Firstly, a hierarchical dependence-aware pruning algorithm was designed to reduce the redundancy of the model. In addition, the resolution of the detection head is increased to 160×160 to enhance the detection ability of small targets, the standard convolution blocks of the network are replaced by GhostConv to reduce the computational redundancy, and the C3 module in the Neck network is redesigned by introducing the compact architecture StarNet to reduce the complexity of the feature fusion process and enhance the feature expression ability. Finally, the attention mechanism is introduced in the backbone layer to improve the feature extraction ability of the model. The experimental results show that in the VisDrone2019 dataset, the mAP_0.5 of the model is increased by 1.8%. At the same time, compared with the original model, the number of parameters is reduced by 50.4%, and the amount of computation is reduced by 35.44%. In summary, the model satisfies the requirements of the UAV platform for accuracy and lightweight in small target detection tasks.