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<p>Song-dynasty bronze-mirror pattern recognition has largely relied on subjective expert judgment, limiting both efficiency and accuracy. Here we develop an automated framework that integrates ResNet50 with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to identify animal motifs. We curate a dataset of bronze-mirror images covering 14 animal-pattern categories and use data augmentation to mitigate limited sample size and improve generalization. A ResNet50 classifier is trained and its hyperparameters are jointly optimized via MOEA/D, balancing performance objectives. The optimized model achieves a maximum Hamming accuracy of 94.48% on the validation set and shows improved predictive consistency. Macro-F1, which is sensitive to minority classes, varies modestly but overall generalization is strengthened. On the test set, the approach remains computationally tractable and outperforms comparative models. This multi-objective optimization strategy offers a robust route for automated identification and authentication of complex traditional decorative motifs, supporting broader dissemination and reuse in heritage-science workflows.</p>

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Research on Song dynasty copper mirror pattern recognition based on MOEAD

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月16日

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  • Qing Feng,
  • Kexin Yu,
  • Yaxuan Li,
  • Tian Ma

摘要

Song-dynasty bronze-mirror pattern recognition has largely relied on subjective expert judgment, limiting both efficiency and accuracy. Here we develop an automated framework that integrates ResNet50 with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to identify animal motifs. We curate a dataset of bronze-mirror images covering 14 animal-pattern categories and use data augmentation to mitigate limited sample size and improve generalization. A ResNet50 classifier is trained and its hyperparameters are jointly optimized via MOEA/D, balancing performance objectives. The optimized model achieves a maximum Hamming accuracy of 94.48% on the validation set and shows improved predictive consistency. Macro-F1, which is sensitive to minority classes, varies modestly but overall generalization is strengthened. On the test set, the approach remains computationally tractable and outperforms comparative models. This multi-objective optimization strategy offers a robust route for automated identification and authentication of complex traditional decorative motifs, supporting broader dissemination and reuse in heritage-science workflows.