SFC-DETR: An Asymmetric Spatial-Frequency Intervention Paradigm for Dense Aerial Object Detection

Abstract

Unmanned aerial vehicle (UAV) object detection struggles with weak and tiny targets due to extreme viewing angles and environmental clutter. While the Real-Time Detection Transformer (RT-DETR) offers end-to-end efficiency, its macroscopic pooling and global attention mechanisms intrinsically discard the local spatial coordinates of tiny objects, causing severe missed detections in dense scenarios. To overcome this, we propose SFC-DETR, an efficient architecture featuring spatial-frequency decoupling and late feature compensation. Specifically, a Spatial-Frequency Decoupling Module (SFDE) is embedded in deep layers to filter low-frequency background noise and purify high-frequency target textures via spatial differential approximation. Serving as a pre-attention purifier, it significantly mitigates background crosstalk. Concurrently, to counteract deep-network resolution decay, a Feature Resolution Entry (FRE) module extracts high-fidelity microscopic features from extremely shallow layers for direct late compensation at the decoder's front end, thoroughly bridging the spatial representation gap. Extensive experiments on the VisDrone2019 dataset demonstrate that SFC-DETR achieves substantial gains in small object precision (mAP_50) and overall accuracy (mAP{50-95}) while maintaining real-time inference speeds, establishing a robust and highly efficient paradigm for weak object recognition in dense UAV scenarios.