Plate-YOLO: An End-to-End Object Detection Framework for Robust Astrometric Registration of Historical Plates

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Plate-YOLO: An End-to-End Object Detection Framework for Robust Astrometric Registration of Historical Plates

Authors

Quanfeng Xu, Zhengjun Shang, Shiyin Shen, Yong Yu, Mingyang Ma, Zhenghong Tang, Meiting Yang, Jing Yang, Jianhai Zhao

Abstract

Historical astronomical plates provide a long temporal baseline for time-domain astrophysics, but degradation and structural artifacts can hinder automated astrometric calibration. We present Plate-YOLO, a framework that combines source detection and stellar classification in one detector and supplies source catalogs to a separate astrometric solver. The detector adapts YOLOv5 with Space-to-Depth feature rearrangement, SimAM attention, a high-resolution P2 head, and a Normalized Wasserstein Distance loss for small bounding boxes. A Gaia-guided fine-tuning procedure uses initially solved plates to construct labels for a new archive. On 150 UBAI plates for which the conventional pipeline had failed, the Chinese-trained model solved 40 plates, and target-archive fine-tuning recovered a further 106, giving an overall recovery rate of 146/150 (97.3%). In the reported hardware configuration, source extraction took 6.61 s per plate for Plate-YOLO and 16.73 s for SExtractor. These results demonstrate the practical value of adapting source detection to degraded historical plates.

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