DOL-DETR: An Efficient Small Object Detection Algorithm for Unmanned Aerial Vehicle Remote Sensing

Object detection in Unmanned Aerial Vehicle (UAV) imagery faces severe challenges, including small target scales, dense spatial distributions, and complex backgrounds. To address the feature attenuation and noise interference inherent in existing deep learning models, this paper proposes DOL-DETR, an efficient small object detection algorithm based on the Real-Time DEtection TRansformer (RT-DETR) architecture. Our model introduces three key innovations. First, the DAttention-based Intra-scale Feature Interaction (DAIFI) module reconstructs intra-scale feature interactions using deformable attention to focus on salient regions with linear complexity. Second, the Omni-Modulated Feature Fusion (OMFF) mechanism adaptively captures multi-scale features and dynamically suppresses background noise. Finally, Linear De-redundancy Convolution (LDConv) replaces standard downsampling to dynamically adapt to object deformations. While introducing a complex dynamic resampling mechanism, it strategically optimizes parameter allocation, significantly enhancing localization precision without introducing excessive computational overhead. Extensive experiments on the VisDrone2019 benchmark demonstrate that DOL-DETR achieves an mAP@0.5 of 52.4% (a 4.2% improvement over the baseline) while maintaining a real-time inference speed of 120.1 FPS with only 20.1M parameters. Furthermore, generalization experiments on the large-scale DOTA dataset yield a 76.1% mAP@0.5, outperforming the baseline by 3.8%. These results indicate that DOL-DETR provides a better trade-off between detection accuracy, inference efficiency, and cross-domain generalization in UAV remote sensing scenarios.