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A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept sin...

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Autores principales: Moradi, Morteza, Ghorbani, Ramin, Sfarra, Stefano, Tax, David M.J., Zarouchas, Dimitrios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740253/
https://www.ncbi.nlm.nih.gov/pubmed/36502062
http://dx.doi.org/10.3390/s22239361
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author Moradi, Morteza
Ghorbani, Ramin
Sfarra, Stefano
Tax, David M.J.
Zarouchas, Dimitrios
author_facet Moradi, Morteza
Ghorbani, Ramin
Sfarra, Stefano
Tax, David M.J.
Zarouchas, Dimitrios
author_sort Moradi, Morteza
collection PubMed
description Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, machine learning and deep learning techniques are effective tools that should be employed properly in accordance with the experiment’s nature and the collected data. Considering both the temporal and spatial perspectives of step-heating thermography, a spatiotemporal deep neural network is developed for defect identification in a mock-up reproducing an artwork. The results are then compared with those of other conventional algorithms, demonstrating that the proposed approach outperforms the others.
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spelling pubmed-97402532022-12-11 A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography Moradi, Morteza Ghorbani, Ramin Sfarra, Stefano Tax, David M.J. Zarouchas, Dimitrios Sensors (Basel) Article Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, machine learning and deep learning techniques are effective tools that should be employed properly in accordance with the experiment’s nature and the collected data. Considering both the temporal and spatial perspectives of step-heating thermography, a spatiotemporal deep neural network is developed for defect identification in a mock-up reproducing an artwork. The results are then compared with those of other conventional algorithms, demonstrating that the proposed approach outperforms the others. MDPI 2022-12-01 /pmc/articles/PMC9740253/ /pubmed/36502062 http://dx.doi.org/10.3390/s22239361 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Moradi, Morteza
Ghorbani, Ramin
Sfarra, Stefano
Tax, David M.J.
Zarouchas, Dimitrios
A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
title A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
title_full A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
title_fullStr A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
title_full_unstemmed A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
title_short A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
title_sort spatiotemporal deep neural network useful for defect identification and reconstruction of artworks using infrared thermography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740253/
https://www.ncbi.nlm.nih.gov/pubmed/36502062
http://dx.doi.org/10.3390/s22239361
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