To address the problems of difficult feature extraction and low detection accuracy caused by small target scales and severe occlusion from the UAV perspective, this paper proposes an improved RT-DETR-based UAV small target detection algorithm named SwiftHawk-DETR. Firstly, the fusion path of the feature pyramid network (FPN) is reconstructed: A tiny target detection layer P2 is added to enhance fine-grained feature capture, and the redundant large target detection layer P5 is removed to simplify computations. Secondly, a deformable fast multi-scale attention network (Dfaster_net) is designed, which strengthens the capture of small target boundary details through a dynamic sampling mechanism while adopting an efficient feature extraction strategy to reduce computational overhead. Thirdly, wavelet feature upgrade (WFU) is introduced into the neck network to alleviate the distortion of edge high-frequency features caused by upsampling and Concat operations in the original model and the slimneck architecture is combined to optimize cross-scale feature fusion. Finally, a loss function based on the weighted fusion of Focaler EIoU and NWD is constructed to improve the small target modeling ability by balancing localization errors and feature distribution differences.Experimental results on the VisDrone2019 and HIT-UAV datasets show that compared with the baseline RT-DETR, the proposed algorithm increases mAP50 and mAP50:95 by 3.8% and 4.2% respectively, and reduces the number of parameters by 61%.