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

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 8; p. 1406
• Main Authors: Li, Zhenghao, Zhou, Xin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2025
• Subjects: Accuracy; Adaptability; Algorithms; auxiliary feature extractors; Classification; Computational linguistics; Datasets; Deep learning; Deformation; Electromagnetism; Feature extraction; Formability; half-window filter; Language processing; multi-scale adapter; Natural language interfaces; Neural networks; Radio communications; Radio signals; refined deformable-DETR; Remote sensing; SAR target detection; Signal detection; Signal processing; Spectrograms; Success; Supervision; Target detection; Target recognition; Task complexity
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17081406

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.