Refined Deformable-DETR for SAR Target Detection and Radio Signal Detection

SAR target detection and signal detection are critical tasks in electromagnetic signal processing, with wide-ranging applications in remote sensing and communication monitoring. However, these tasks are challenged by complex backgrounds, multi-scale target variations, and the limited integration of domain-specific priors into existing deep learning models. To address these challenges, we propose Refined Deformable-DETR, a novel Transformer-based method designed to enhance detection performance in SAR and signal processing scenarios. Our approach integrates three key components, including the Half-Window Filter (HWF) to leverage SAR and signal priors, the Multi-Scale Adapter to ensure robust multi-level feature representation, and Auxiliary Feature Extractors to enhance feature learning. Together, these innovations significantly enhance detection precision and robustness. The Refined Deformable-DETR achieves a mAP of 0.682 on the HRSID dataset and 0.540 on the Spectrograms dataset, demonstrating remarkable performance compared to other methods.