HRTD: A high-precision rotating target detector for SAR scenes

In the past, synthetic aperture radar (SAR) target detection research has mainly focused on the representation of bounding boxes. However, the distant contextual prior knowledge in SAR images has not been fully utilized, leading to challenges in detecting small targets and densely arranged targets with multi-scale characteristics. Therefore, this paper proposes a high-precision rotating target detector (HRTD) for SAR scenarios to address the aforementioned issues. More precisely, we introduce a multi-scale rotation-equivariant network (MRNet) and align rotation-invariant regions of interest to enhance target orientation prediction capability. The multi-scale dynamic global attention (MDGA) in MRNet can dynamically adjust the contextual multi-scale spatial receptive field in real-time, facilitating the comprehensive extraction of distant contextual features. This method achieves significantly better detection performance on the SSDD+ and SRSDD-v1.0 datasets compared to other algorithms.