WS-RTDETR: A Wavelet-Guided RT-DETR for Small Object Detection in Foggy UAV Imagery

Abstract

Accurate small-object detection in UAV imagery is essential for low-altitude autonomous operations, but fog severely degrades image quality and makes it difficult to maintain both detection accuracy and computational efficiency. To address this problem, we propose WS-RTDETR, a robust and efficient detector with a wavelet-guided stage-specific design. Its three components serve distinct roles across the network: the Frequency-Domain Decoupling Embedding Module (FDEM) suppresses fog-dominant interference during early downsampling while preserving fragile small-object structures, the Small Object Enhancement Pyramid (SOEP) strengthens shallow high-resolution features for tiny-target representation, and the Wavelet-Guided Selective Encoder (WGSE) performs wavelet-guided dual-stage spatial refinement to emphasize informative regions and suppress redundant background responses. Experiments on the synthetic VisDrone2019-Fog dataset show that WS-RTDETR achieves 38.2% mAP 50 and 22.3% mAP 50:95 , outperforming RT-DETR-R18 by 2.9 and 1.9 points, respectively, while improving AP S by 2.6 points at 64.3 GFLOPs and 22.5M parameters. Additional results on HazyDet further demonstrate strong transfer from synthetic fog to real-world hazy UAV imagery.