Aiming at the problems in UAV aerial images, such as complex illumination interference, low resolution of small targets with high susceptibility to missing detection and false detection and blurred target structures, a target detection method for UAV aerial images based on improved RT-DETR is proposed, denoted as IF-DETR. Firstly, a cross-stage illumination gating unit is designed in the backbone network, which enhances the model′s ability to extract targets under complex illumination environments by modeling illumination and reflection components at the feature level. Secondly, a multi-scale feature fusion structure for small targets is constructed in the neck network, effectively improving the feature extraction and recognition capabilities for small targets. In addition, a Gaussian feature enhancement module is proposed, which utilizes Gaussian prior and spatial attention mechanism to enhance target-related structural information while suppressing background noise. Finally, a Focaler-Powerful-IoU loss function is constructed to achieve more stable and accurate target localization. Experimental results show that compared with the RT-DETR model, the improved IF-DETR algorithm on the Visdrone2019 dataset achieves improvements of 3.0%, 2.3%, 3.9% and 2.0% in mAP50, mAP50:95, recall and precision respectively. It can effectively alleviate the problems of missing detection and false detection in UAV aerial image detection, improve detection performance, and has broad application prospects.