FE-DETR: A Fourier-Enhanced, Edge-Aware Framework for UAV-Based Remote Sensing Object Detection

Aerial object detection in drone-based imagery presents unique challenges including sub-20px targets, motion blur, dense occlusions, and complex backgrounds. Existing methods struggle to harmonize spectral sensitivity with spatial precision while maintaining real-time efficiency. This paper pro-poses FE-DETR, an optimized end-to-end framework integrating Fourier-enhanced processing, adaptive attention, and edge-aware fusion. First, the Fourier-Enhanced Feature Fusion FFF module synergizes global fre-quency analysis with multi-scale dilated convolutions, amplifying faint ob-ject signatures while preserving structural integrity under motion blur. Sec-ond, the Adaptive WL-GH Attention dynamically allocates computation be-tween local window attention and global cross-window reasoning via learna-ble feature statistics. Third, the Edge-Enhanced Multi-Scale Fusion neck E²MF embeds physics-inspired Sobel operators to maintain structural co-herence in occlusion-heavy scenes. Evaluated on VisDrone2019, FE-DETR achieves state-of-the-art 50.4 mAP50 and 31.1 mAP50-95 with 17.3M parameters and 54.9G FLOPs. Ablation studies confirm the complementary benefits of spectral-spatial fusion and edge-aware processing. The framework demonstrates robust performance across illumination variations and scale disparities, offering practical efficiency for UAV deployment. Code will be released at https: github.com Avery5233 FE-DETR.