Reliable Real-Time SAR Target Detection Requires Physics-Preserving Data Augmentations

Synthetic Aperture Radar (SAR) supports all-weather, day-and-night Earth observation, making it vital for military and civilian applications. While deep learning has advanced SAR target detection, state-of-the-art (SOTA) methods often directly adopt optical data augmentations, such as rotation, scaling, and Mixup, which violate SAR imaging physics and lead to artifacts, resolution distortion, and physically implausible scattering patterns. This work reveals that such physics-violating augmentations consistently compromise the detection reliability of real-time YOLO-series detectors, with the most severe drops occurring under strict localization criteria corresponding to higher IoU thresholds. To address this issue, we propose physics-preserving augmentations (PPA) that respect SAR domain priors. Extensive experiments on three representative datasets (SAR-AIRcraft-1.0, SIVED, and SRSDD) demonstrate the consistent superiority of PPA over optical augmentations in the context of SAR target detection. Notably, PPA enables the lightweight YOLOv11n to achieve 79.2% mAP75 on SAR-AIRcraft-1.0, comparable to or superior to existing SOTA SAR detectors while retaining real-time efficiency.