To address the issues of low precision, false detections and missed detections for small objects in UAV aerial images caused by large scale variations and complex backgrounds, an improved algorithm named CSFF-DETR based on RT-DETR is proposed. First, a context-aware enhancement module is designed in the backbone to improve small object feature extraction and suppress complex background interference. Second, a high-resolution P2 feature layer is introduced into the neck network, along with a novel cross-scale feature fusion module, to enhance interaction between features at different scales and boost small object detection capability. Finally, a multi-path fusion downsampling module is designed to better preserve and extract fine-grained details of small objects. Experiments on the VisDrone2019, Tinyperson and UAVVaste datasets show that compared to the baseline RT-DETR model, CSFF-DETR achieves mAP50 improvements of 3.6%, 1.0% and 2.8% and mAP50.95 improvements of 3.0%, 1.1% and 1.6%, respectively, while reducing parameters by 40.7%. This meets the requirements for both detection accuracy and lightweight deployment on UAV platforms.