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Volume 13
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Article

Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence

by
Bengin Bilici Genc
1,
Erkan Bostanci
2,
Bekir Eskici
1,
Hakan Erten
2,
Berna Caglar Eryurt
1,
Koray Acici
3,
Didem Ketenoglu
4 and
Tunc Asuroglu
5,6,*
1
Department of Conservation and Restoration of Cultural Properties, Ankara Hacı Bayram Veli University, Ankara 06830, Turkey
2
Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
3
Faculty of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
4
Department of Physis Engineering, Ankara University, Ankara 06830, Turkey
5
Department of Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
6
VTT Technical Research Centre of Finland, 33101 Tampere, Finland
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(20), 4039; https://doi.org/10.3390/electronics13204039
Submission received: 13 September 2024 / Revised: 2 October 2024 / Accepted: 11 October 2024 / Published: 14 October 2024
(This article belongs to the Special Issue Electronics and Computer Science for Cultural Heritage: Advancements, Preservation, and Applications)
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Abstract

Cultural assets are all movable and immovable assets that have been the subject of social life in historical periods, have unique scientific and cultural value, and are located above ground, underground or underwater. Today, the fact that most of the analyses conducted to understand the technologies of these assets require sampling and that non-destructive methods that allow analysis without taking samples are costly is a problem for cultural heritage workers. In this study, which was prepared to find solutions to national and international problems, it is aimed to develop a non-destructive, cost-minimizing and easy-to-use analysis method. Since this article aimed to develop methodology, the materials were prepared for preliminary research purposes. Therefore, it was limited to four primary colors. These four primary colors were red and yellow ochre, green earth, Egyptian blue and ultramarine blue. These pigments were used with different binders. The produced paints were photographed in natural and artificial light at different light intensities and brought to a 256 × 256 pixel size, and then trained on support vector machine, convolutional neural network, densely connected convolutional network, residual network 50 and visual geometry group 19 models. It was asked whether the trained VGG19 model could classify the paints used in archaeological and artistic works analyzed with instrumental methods in the literature with their real identities. As a result of the test, the model was able to classify paints in artworks from photographs non-destructively with a 99% success rate, similar to the result of the McNemar test.
Keywords: cultural properties; paint technology; artificial intelligence; non-destructive analysis; instrumental analysis cultural properties; paint technology; artificial intelligence; non-destructive analysis; instrumental analysis

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MDPI and ACS Style

Bilici Genc, B.; Bostanci, E.; Eskici, B.; Erten, H.; Caglar Eryurt, B.; Acici, K.; Ketenoglu, D.; Asuroglu, T. Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence. Electronics 2024, 13, 4039. https://doi.org/10.3390/electronics13204039

AMA Style

Bilici Genc B, Bostanci E, Eskici B, Erten H, Caglar Eryurt B, Acici K, Ketenoglu D, Asuroglu T. Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence. Electronics. 2024; 13(20):4039. https://doi.org/10.3390/electronics13204039

Chicago/Turabian Style

Bilici Genc, Bengin, Erkan Bostanci, Bekir Eskici, Hakan Erten, Berna Caglar Eryurt, Koray Acici, Didem Ketenoglu, and Tunc Asuroglu. 2024. "Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence" Electronics 13, no. 20: 4039. https://doi.org/10.3390/electronics13204039

APA Style

Bilici Genc, B., Bostanci, E., Eskici, B., Erten, H., Caglar Eryurt, B., Acici, K., Ketenoglu, D., & Asuroglu, T. (2024). Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence. Electronics, 13(20), 4039. https://doi.org/10.3390/electronics13204039

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