Infrared thermal defect identification and reconstruction of artworks using a spatiotemporal deep neural network

Conference paper (2022)

Authors

M. Moradi Structural Integrity & Composites - Aerospace Engineering
R. Ghorbani Pattern Recognition and Bioinformatics - EEMCS
Stefano Sfarra University of L'Aquila
D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS
D. Zarouchas Structural Integrity & Composites - Aerospace Engineering
Research Group
Structural Integrity & Composites (Aerospace Engineering) (TU Delft)
Copyright: © 2022 M. Moradi, R. Ghorbani, Stefano Sfarra, D.M.J. Tax, D. Zarouchas
Deep Learning U-Net Damage Detection Cultural heritage MLP neural network Convolutional Neural Network (CNN) Infrared thermography Spatiotemporal deep neural network Non-destructive method (NDT) Artworks
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Published Date

2022

Language

English

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Faculty

Aerospace Engineering

Department

Aerospace Structures & Materials

Research Group

Structural Integrity & Composites

Abstract

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) 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, a spatiotemporal deep neural network (STDNN) is utilized for defect identification in a mock-up reproducing an artwork, taking into account both the temporal and spatial perspectives of step-heating (SH) thermography. Finally, the outcomes are compared to those of other conventional algorithms.