SARoLO: Structure and Scale-Aware SAR Object Detection

Synthetic Aperture Radar (SAR) object detection faces persistent challenges caused by small-target dominance, strong speckle noise, and complex background clutter that obscure fine-grained structural cues. Although conventional YOLO detectors excel in general-purpose visual tasks with balanced accuracy and efficiency, they remain limited in representing scale- and structure-sensitive features due to their reliance on aggressive downsampling, information-lossy bottleneck compression, and redundant multi-scale detection heads. To address these limitations, we propose a novel structure and scale-aware SAR object detection method, called SARoLO, including a Heterogeneous Triple-Conv Downsampling (HetTriDown) module, a Heterogeneous Convolution Modulation Block (HCM Block) with dynamic nonlinear modulation (DyN), a cross-stage feature aggregation module (HCMC3K), a Cross-Scale Aggregation Network (CSAN), and a dual-scale detection head, to optimize bounding box performance for SAR imagery characterized by small/medium targets and significant structural variations. Extensive experiments on the HRSID and SSDD benchmarks demonstrate SARoLO's state-of-the-art performance. On HRSID, SARoLO achieves 68.46% AP, a 2.52% gain over the latest YOLOv13 baseline. On SSDD, SARoLO attains 63.21% AP, a 0.9% improvement over YOLOv13. These results validate its effectiveness in structure- and scale-aware feature learning and efficient SAR object detection.