FaSDiNet: An End-to-End Efficient Network for Oracle Bone Fragment Rejoining Driven by Full-Image Candidate Screening

Oracle bone rejoining is central to oracle bone studies and digital cultural heritage curation, but many computer-assisted methods still rely on manually selected contour curves, candidate fracture edges, or local preprocessing. We introduce the Fast Siamese Diffusion Network for Oracle Bone Fragment Rejoining (FaSDiNet), a FasterNet-based Siamese framework with a latent-diffusion-based data augmentation module for full-fragment-image candidate screening and assisted rejoining of oracle bone fragments. FaSDiNet combines latent diffusion augmentation, a FasterNet-based Siamese feature encoder, and joint optimization of contrastive loss and binary cross-entropy loss to improve pairwise matching under limited training data. On the self-built OBF dataset, FaSDiNet achieves Top-1, Top-5, Top-10, and Top-15 recall values of 23.68%, 60.53%, 76.32%, and 84.21%, respectively. Among the evaluated OBF methods, its strongest comparative performance appears at broader candidate ranges, where it obtains the best Top-5, Top-10, and Top-15 recall values. On the public COBD dataset, FaSDiNet achieves Top-10 and Top-15 recall values of 93.8% and 96.9%, respectively, showing competitive candidate-retention performance at larger candidate ranges. These quantitative results indicate that FaSDiNet is especially effective for preserving truly rejoinable fragments within manageable ranked candidate lists, supporting efficient expert-assisted screening and offering a practical visual-computing workflow for oracle bone collation.