Hyperspectral Imaging Combined with Deep Learning for the Detection of Mold Diseases on Paper Cultural Relics

Mold contamination is one of the critical factors threatening the safety of paper-based cultural relics. Current detection methods rely predominantly on offline analysis, facing challenges such as low efficiency and limited real-time accuracy, which hinder their effectiveness in meeting the technical requirements of cultural heritage preventive conservation. This study proposes a hyperspectral imaging (HSI)-deep learning integrated fungal segmentation framework for deterioration detection in paper-based artifacts. Firstly, the HSI data was reduced to three dimensions via Locally Linear Embedding (LLE) manifold learning to construct 3D pseudo-color imagery, effectively preserving discriminative spectral features between fungal colonies and substrates while eliminating spectral redundancy. Secondly, a hybrid architecture synergizing Feature Pyramid Networks (FPN) with Vision Transformers was developed for semantic segmentation, leveraging CNN’s local feature extraction and Transformer’s global context modeling to enhance fungal signature saliency and suppress background interference. Innovatively, a dynamic sparse attention mechanism is introduced, optimizing attention allocation through the TOP-K algorithm to screen regions richer in mold information spatially and spectrally, thereby improving segmentation accuracy. Semantic segmentation experiments were conducted on papers infected with different molds. The results demonstrate that the proposed method achieves excellent performance in mold segmentation, providing technical support for mold detection and preventive conservation of cultural relics.