An algorithm has been proposed to detect small targets in unmanned aerial vehicle(UAV) aerial images. The algorithm is based on an improved real-time detection Transformer (RT-DETR) and aims to address the challenges posed by complex backgrounds and a large number of small target samples. To enhance the feature fusion network, a dedicated feature fusion structure for small targets has been incorporated, utilizing rich location information from the shallow feature map to improve the network's ability to detect small targets. Furthermore, the last residual block in the BackBone has been removed to prevent an increase in additional parameters. Additionally, the MCP Block, a reconstructed BasicBlock structure in the backbone network, has been designed, which includes a multi-channel feature partial convolution module (MCPConv) to reduce redundancy in channel features and enhance the acquisition of multi-scale detail features. Moreover, a location encoding mechanism with learning ability has been introduced to obtain more accurate and expressive location information. The normalized weighted deviation (NWD) and mean precision-driven IoU(MPDIoU) positioning loss functions have been incorporated to accelerate the convergence speed of the model and reduce sensitivity to position deviation. Experimental results on the VisDrone2019-DET dataset demonstrate that the improved model reduces parameters by 62% compared to the original model, increases mAP50 by 3.9%, and improves FPS by 17%. The improved model exhibits superior detection performance compared to other mainstream detection models.