Watershed YOLO: Method for Ordered Recognition of Microwave Photonic Radar Scatter Points Based on YOLO and Peak-Constrained Watershed Algorithm

To address the challenge of achieving high-precision and ordered calibration of strong-scatter points in inverse synthetic aperture radar (ISAR) images, this paper proposes a collaborative framework that integrates YOLOv12-pose with Peak-Constrained Watershed (PCW). The method first employs the YOLOv12-pose model to produce an initial localization of scatter points. PCW is then applied to fine-segment individual points. Finally, a three-stage global optimal matching strategy is introduced to achieve high-precision fusion between index labels and their geometric positions. Experimental results on a microwave photonic radar ISAR dataset demonstrated that the proposed method achieved an average error of 1.89 pixels, with accuracy, recall, and F1 scores exceeding 95%. The approach significantly outperformed standalone YOLO, Mask R-CNN, and traditional SVM-based methods while maintaining label consistency and substantially improving precision and robustness for the recognition, localization, and tracking of strong scatter points in ISAR imagery.