Object detection in Synthetic Aperture Radar (SAR) images remains a challenging task due to factors such as complex backgrounds, frequent fluctuations in object scale and quantity, and the inherent discrete scattering characteristics of SAR imaging. To address these challenges, we propose a DETR (DEtection TRansformer) with Corner-Guided and Adaptive Query for SAR Object Detection, which integrates a Corner-Guided Multi-Scale Feature Enhancement Module (CMFE) and an Adaptive Query Regression Module (AQR). The CMFE module processes multi-scale features by detecting and clustering corners to assess the scale and quantity of objects, which are used to compute the importance weights of features at different scales. The AQR module regresses the number of object queries by evaluating the rough object count from the low-level features, thereby achieving more precise and adaptive query allocation. Both modules are supervised by real data. Extensive experiments conducted on the SARDet-100K and FAIR-CSAR datasets demonstrate that our method achieves SOTA (state-of-the-art) performance, and achieved mAP@50 scores of 69.8% and 92.9%, validating its effectiveness and practical applicability in SAR object detection.
Zuo et al. (Sun,) studied this question.