WDFS-DETR: A Transformer-based framework with multi-scale attention for small object detection in UAV Engineering Tasks - Jinjiang Liu; Yonghua Xie (Results in Engineering)
With the growing adoption of Unmanned Aerial Vehicles (UAVs) in surveillance, emergency response, and environmental monitoring, accurate small object detection under resource constraints remains a critical challenge. To address this issue, we propose WDFS-DETR (Wavelet-based Dual-stage Feature-enhanced Detection Transformer), a Transformer-based detection framework built upon RT-DETR (Real-Time Detection Transformer). The framework integrates four custom-designed components to jointly enhance detection accuracy and efficiency. First, BasicBlock-WTCM (Wavelet-based Transform Coordinate Module) improves small object perception by modeling spatial and channel semantics across scales. Second, the Dual-Stage Adaptive Normalization (DSAN) dynamically selects appropriate normalization strategies during training and inference to improve convergence and runtime performance. Third, the Feature-Focused Diffusion Pyramid Network (FFDPN) enhances context modeling and robustness via hierarchical multi-scale feature alignment and fusion. Finally, the boundary-aware Slide-VarifocalLoss combines sliding window mechanisms with class-weighted reweighting to address class imbalance and improve boundary localization. Experiments show that WDFS-DETR improves mAP@0.5 by 2.2 % over RT-DETR-r18 on VisDrone2019. When deployed on Jetson Orin Nano, inference speed improves from 38.6 FPS to 50.1 FPS, highlighting its suitability for real-time deployment on lightweight platforms. The model also generalizes well to UAVDT and DOTA datasets, demonstrating its applicability to real-world UAV-based detection tasks in complex engineering settings. Source code is available at: https://github.com/liuliuliu2002/WDFS-DETR.