Mold Spot Detection for Paper Artifacts Based on Multimodal Feature Fusion

As an important carrier of Chinese civilization, the prevention and control of mold growth in paper cultural relics is the core challenge in the field of cultural relic protection. To address the issues of low sensitivity and poor timeliness of traditional detection methods, this study proposes a TriplePath Multimodal Feature Fusion Network (TPMFN) based on hyperspectral imaging. Our research constructs an improved two-dimensional convolutional network (2D-CNN), extracts RGB bands using spatial attention mechanism, and introduces improved SpectralFormer module to accurately capture mold specific responses in the 400-1000nm spectral range, ultimately achieving efficient integration and classification decision-making of multi-source information. In this study six categories of typical fungal colonization samples from paper-based cultural relics were analyzed. The research results indicate that the proposed TPMFN has significant advantages over baseline models including including Support Vector Machine (SVM), 1D-CNN, 2D-CNN, SSFTT, HybridSN and SpectralFormer, achieving 98.84% overall accuracy and 98.54% kappa coefficient. Notably, it attained a 3.6% improvement in detailed feature identification accuracy compared to the suboptimal baseline. The ablation study with component-wise evaluation revealed that the dual-attention mechanism enhanced feature discriminative power with a relative increase of 7.5%. This study provides a high-precision and practical solution for mold detection of paper cultural relics, which has important practical value for establishing a preventive protection system