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<p>Chinese paper-based cultural artifacts are threatened by mold deterioration, necessitating precise mold identification for effective conservation. This study introduces a hyperspectral imaging-based triplePath multimodal feature fusion network (TPMFN) to improve the sensitivity and timeliness of traditional methods. It combines spatial and spectral features using an advanced 2-D CNN with spatial attention for RGB details, a SpectralFormer for analyzing 400–1000 nm hyperspectral data, and a hybridCNN for extracting spectral-spatial features. Various cross-modal fusions (concatenation, addition, multiplication) facilitate efficient integration and classification of multi-source data. Testing six mold types on paper artifacts, TPMFN significantly outperforms SVM, 1-D CNN, 2-D CNN, SSFTT, HybridSN, and SpectralFormer on OA (98.78%), AA (98.52%), and Kappa (98.21%), with a 3.83% improvement in detail recognition. Ablation studies confirmed a 7.5% feature discriminability enhancement from the dual attention mechanism. This research provides a high-precision, practical solution for paper artifact mildew detection, crucial for establishing preventative conservation.</p>

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Mold spot detection for paper artifacts based on multimodal feature fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月25日

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  • Xuexu Deng,
  • Ya Zhao,
  • Dan Qin,
  • Zheng Ma,
  • Zhongyu Xiao,
  • Xiling Luo,
  • Mingfu Zhao,
  • Tao Song,
  • Jianxu Wang,
  • Bin Tang,
  • Huan Tang

摘要

Chinese paper-based cultural artifacts are threatened by mold deterioration, necessitating precise mold identification for effective conservation. This study introduces a hyperspectral imaging-based triplePath multimodal feature fusion network (TPMFN) to improve the sensitivity and timeliness of traditional methods. It combines spatial and spectral features using an advanced 2-D CNN with spatial attention for RGB details, a SpectralFormer for analyzing 400–1000 nm hyperspectral data, and a hybridCNN for extracting spectral-spatial features. Various cross-modal fusions (concatenation, addition, multiplication) facilitate efficient integration and classification of multi-source data. Testing six mold types on paper artifacts, TPMFN significantly outperforms SVM, 1-D CNN, 2-D CNN, SSFTT, HybridSN, and SpectralFormer on OA (98.78%), AA (98.52%), and Kappa (98.21%), with a 3.83% improvement in detail recognition. Ablation studies confirmed a 7.5% feature discriminability enhancement from the dual attention mechanism. This research provides a high-precision, practical solution for paper artifact mildew detection, crucial for establishing preventative conservation.