A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
Morteza Moradi (TU Delft - Structural Integrity & Composites)
R. Ghorbani (TU Delft - Pattern Recognition and Bioinformatics)
Stefano Sfarra (University of L'Aquila)
David M. J. Tax (TU Delft - Pattern Recognition and Bioinformatics)
Dimitrios Zarouchas (TU Delft - Structural Integrity & Composites)
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Abstract
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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