In order to solve the problems of low detection accuracy and poor real-time performance caused by small size, dense distribution and complex background of UAV aerial vehicle detection in UAV aerial vehicle detection scenes, this paper proposes an improved UAV aerial vehicle detection algorithm based on YOLOv11n. Firstly, a detection head was added to the high-resolution feature layer of the YOlOv11n backbone network to reduce the problem of small target information loss caused by the reduction of resolution after downsampling, and improve the detection accuracy of small target. Secondly, the Inner-IoU Loss is used to replace the traditional IoU Loss, and the auxiliary box is used to accelerate the convergence of the regression process. In addition, the Multi-scale Attention Aggregation Module (MSAA) is introduced to fuse multi-scale feature information by using spatial and channel attention mechanisms, which can improve the effect of multi-scale spatial and channel fusion while reducing background interference. Experiments show that the improved algorithm achieves a of 38.302% on the VisDrone dataset, which is 5.735% higher than that of the benchmark model.