Research on detection method of foxing on paper cultural relics based on CNN and hyperspectral

Hang Liu; Bin Tang; Zourong Long; Jianxu Wang; Qing Chen; Junfeng Miao; Jinfu Zhang; Mingfu Zhao; Nianbing Zhong; Huan Tang

doi:10.1117/12.2656645

20 October 2022 Research on detection method of foxing on paper cultural relics based on CNN and hyperspectral

Hang Liu, Bin Tang, Zourong Long, Jianxu Wang, Qing Chen, Junfeng Miao, Jinfu Zhang, Mingfu Zhao, Nianbing Zhong, Huan Tang

Author Affiliations +

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511C (2022) https://doi.org/10.1117/12.2656645
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Paper cultural relics are one of the most important cultural carriers of ancient human civilization and precious material materials. Due to age and human factors, a large amount of paper cultural heritage is being corroded by foxing, which seriously affects the safety of paper cultural heritage. At present, the detection and control of insect and mould diseases on paper cultural relics is still a recognized problem in the world. In order to solve the practical problem of foxing detection on paper cultural relics, this paper proposes a paper cultural relic foxing based on one-dimensional convolutional neural network based on the characteristics of nondestructive detection of hyperspectral imaging, aiming at the spectral characteristics difference between the foxing part and the healthy part. The spot detection method realizes the non-destructive detection of foxing on paper cultural relics. The experimental results show that the classification accuracy of the model in this paper is 99.17% for the foxing area and healthy area of paper cultural relics. Compared with traditional methods such as K nearest neighbor method and support vector machine, the convolutional neural network model can fully extract the deep spectral features of the foxing region, and can effectively avoid overfitting. The method of combining deep learning theory with hyperspectral provides a methodological basis for the protection of paper cultural relics.

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Hang Liu, Bin Tang, Zourong Long, Jianxu Wang, Qing Chen, Junfeng Miao, Jinfu Zhang, Mingfu Zhao, Nianbing Zhong, and Huan Tang "Research on detection method of foxing on paper cultural relics based on CNN and hyperspectral", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511C (20 October 2022); https://doi.org/10.1117/12.2656645

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KEYWORDS

Data modeling

Hyperspectral imaging

Convolutional neural networks

Feature extraction

Nondestructive evaluation

Principal component analysis

Convolution

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