To address the challenges of limited feature extraction capability, dense distribution of small objects, and drastic target scale variations that lead to low detection accuracy in unmanned aerial vehicle (UAV) aerial imagery, this paper proposes an improved UAV object detection algorithm based on real-time detection transformer (RT-DETR). Firstly, in the backbone network, a C2f-EncoderBlock feature enhancement module is designed to restructure the network architecture. By introducing a frequency-spatial domain collaborative perception mechanism and utilizing fast Fourier transform （FFT） to capture global illumination and texture information in the frequency domain, this module effectively resolves the difficulty of feature extraction in low-resolution and blurred images while significantly reducing the number of model parameters. In the detection head, a multi-scale small object enhancement pyramid structure is designed to effectively address the issues of missed detections for small objects and coarse feature fusion in the original model through lossless downsampling and multi-dimensional feature interaction. Finally, a focaler minimum point distance intersection over union (Focaler-MPDIoU) loss function incorporating a hard-sample focusing mechanism is designed to replace the original generalized intersection over union (GIoU). This function not only improves the geometric localization accuracy of dense targets by minimizing corner distances but also enhances the model′s ability to mine hard samples, such as tiny or occluded objects, through a dynamic re-weighting strategy, thereby further improving detection accuracy. Experimental results on the VisDrone2019 dataset demonstrate that the improved algorithm achieves a 3.9% increase in mAP50 compared to the baseline model, while reducing the parameter count by 24.75%, effectively enhancing UAV object detection performance.