KEYWORDS: Data modeling, Performance modeling, Evolutionary algorithms, Neural networks, Principal component analysis, Data processing, Spectral data processing, Education and training, Machine learning, Tunable filters
Paper cultural relics are important carriers of splendid history and culture, and have important historical research value. As paper is mainly rich in cellulose, starch and protein, paper cultural relics are prone to mould, insects and other microorganisms in the process of long-term preservation, leading to corrosion, deterioration and even destruction of cultural relics. Fumigation method is currently more widely used in a rapid means of control of cultural relics of mould and mildew, fumigant residue detection is the establishment of a set of scientific fumigation method in an indispensable part. In this paper, for the surface of paper cultural relics there are fumigant residues and no residues of spectral characteristics of the variability, based on the characteristics of spectral nondestructive testing, using BP neural network algorithm, SVM algorithm, KNN algorithm, 1D-CNN algorithm were established to establish discriminatory models, according to the different models of the discrimination accuracy of the model performance assessment, select the optimal modelling method.
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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