{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:14:32Z","timestamp":1781518472534,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s22239361","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T03:57:28Z","timestamp":1669867048000},"page":"9361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2543-0766","authenticated-orcid":false,"given":"Morteza","family":"Moradi","sequence":"first","affiliation":[{"name":"Structural Integrity & Composites Group, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands"},{"name":"Center of Excellence in Artificial Intelligence for Structures, Prognostics & Health Management, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3631-0177","authenticated-orcid":false,"given":"Ramin","family":"Ghorbani","sequence":"additional","affiliation":[{"name":"Pattern Recognition Laboratory, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9354-4650","authenticated-orcid":false,"given":"Stefano","family":"Sfarra","sequence":"additional","affiliation":[{"name":"Department of Industrial and Information Engineering and Economics (DIIIE), University of L\u2019Aquila, Piazzale E. Pontieri 1, Monteluco di Roio, 67100 L\u2019Aquila, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David M.J.","family":"Tax","sequence":"additional","affiliation":[{"name":"Pattern Recognition Laboratory, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Zarouchas","sequence":"additional","affiliation":[{"name":"Structural Integrity & Composites Group, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands"},{"name":"Center of Excellence in Artificial Intelligence for Structures, Prognostics & Health Management, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1134\/S1061830913040062","article-title":"Thermographic, ultrasonic and optical methods: A new dimension in veneered wood diagnostics","volume":"49","author":"Sfarra","year":"2013","journal-title":"Russ. J. Nondestruct. Test."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/17686733.2020.1799304","article-title":"Quantitative measurement of cast metal relics by pulsed thermal imaging","volume":"19","author":"Tao","year":"2020","journal-title":"Quant. Infrared Thermogr. J."},{"key":"ref_3","first-page":"115","article-title":"The Boxer at Rest and the Hellenistic Prince: A comparative thermographic study","volume":"24","author":"Orazi","year":"2019","journal-title":"J. Archaeol. Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103718","DOI":"10.1016\/j.infrared.2021.103718","article-title":"Rectifying the emissivity variations problem caused by pigments in artworks inspected by infrared thermography: A simple, useful, effective, and optimized approach for the cultural heritage field","volume":"115","author":"Moradi","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/0031-9155\/15\/1\/301","article-title":"The thermal scanning of a curved isothermal surface: Implications for clinical thermography","volume":"15","author":"Watmough","year":"1970","journal-title":"Phys. Med. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1007\/s10973-018-7644-6","article-title":"Thermography data fusion and nonnegative matrix factorization for the evaluation of cultural heritage objects and buildings","volume":"136","author":"Yousefi","year":"2019","journal-title":"J. Therm. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3835","DOI":"10.1007\/s10973-020-09326-2","article-title":"Evaluating quality of marquetries by applying active IR thermography and advanced signal processing","volume":"143","author":"Chulkov","year":"2021","journal-title":"J. Therm. Anal."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.culher.2003.07.002","article-title":"Applications of infrared thermography for the investigation of historic structures","volume":"5","author":"Avdelidis","year":"2004","journal-title":"J. Cult. Herit."},{"key":"ref_9","unstructured":"Maldague, X.P. (2012). Nondestructive Evaluation of Materials by Infrared Thermography, Springer Science & Business Media."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103754","DOI":"10.1016\/j.infrared.2021.103754","article-title":"Infrared machine vision and infrared thermography with deep learning: A review","volume":"116","author":"He","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102164","DOI":"10.1016\/j.ndteint.2019.102164","article-title":"Temporal and spatial deep learning network for infrared thermal defect detection","volume":"108","author":"Luo","year":"2019","journal-title":"NDT E Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deane, S., Avdelidis, N.P., Ibarra-Castanedo, C., Williamson, A.A., Withers, S., Zolotas, A., Maldague, X.P.V., Ahmadi, M., Pant, S., and Genest, M. (2022). Development of a thermal excitation source used in an active thermographic UAV platform. Quant. Infrared Thermogr. J., 1\u201332.","DOI":"10.1080\/17686733.2022.2056987"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, C., Park, G., Jang, H., and Kim, E.-J. (2022). Automated classification of thermal defects in the building envelope using thermal and visible images. Quant. Infrared Thermogr. J., 1\u201317.","DOI":"10.1080\/17686733.2022.2033531"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.infrared.2019.03.003","article-title":"Numerical and experimental study for assessing stress in carbon epoxy composites using thermography","volume":"98","author":"Bayat","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/17686733.2021.1953226","article-title":"Detection and characterisation of short fatigue cracks by inductive thermography","volume":"19","year":"2022","journal-title":"Quant. Infrared Thermogr. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1049\/hve2.12023","article-title":"Infrared thermography-based diagnostics on power equipment: State-of-the-art","volume":"6","author":"Xia","year":"2020","journal-title":"High Volt."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109014","DOI":"10.1016\/j.ymssp.2022.109014","article-title":"Spatiotemporal denoising wavelet network for infrared thermography-based machine prognostics integrating ensemble uncertainty","volume":"173","author":"Jiang","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.culher.2019.05.005","article-title":"Development of integrated innovative techniques for paintings examination: The case studies of the resurrection of Christ attributed to andrea mantegna and the crucifixion of viterbo attributed to michelangelo\u2019s workshop","volume":"40","author":"Laureti","year":"2019","journal-title":"J. Cult. Herit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Madruga, F.J., Sfarra, S., Perilli, S., Pivar\u010diov\u00e1, E., and L\u00f3pez-Higuera, J.M. (2020). Measuring the Water Content in Wood Using Step-Heating Thermography and Speckle Patterns-Preliminary Results. Sensors, 20.","DOI":"10.3390\/s20010316"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.infrared.2019.03.017","article-title":"Evaluation of an ancient cast-iron Buddha head by step-heating infrared thermography","volume":"98","author":"Li","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102153","DOI":"10.1016\/j.ndteint.2019.102153","article-title":"Detection of edge debonding in composite patch using novel post processing method of thermography","volume":"107","author":"Moradi","year":"2019","journal-title":"NDT E Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.compscitech.2019.04.031","article-title":"Edge disbond detection of carbon\/epoxy repair patch on aluminum using thermography","volume":"179","author":"Moradi","year":"2019","journal-title":"Compos. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3166\/qirt.7.85-114","article-title":"Diagnostics of panel paintings using holographic interferometry and pulsed thermography","volume":"7","author":"Sfarra","year":"2010","journal-title":"Quant. Infrared Thermogr. J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Laureti, S., Malekmohammadi, H., Rizwan, M.K., Burrascano, P., Sfarra, S., Mostacci, M., and Ricci, M. (2019). Looking Through Paintings by Combining Hyper-Spectral Imaging and Pulse-Compression Thermography. Sensors, 19.","DOI":"10.3390\/s19194335"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, K., Huang, K.-L., Sfarra, S., Yang, J., Liu, Y., and Yao, Y. (2021). Factor analysis thermography for defect detection of panel paintings. Quant. Infrared Thermogr. J., 1\u201313.","DOI":"10.1080\/17686733.2021.2019658"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Garrido, I., Erazo-Aux, J., Lag\u00fcela, S., Sfarra, S., Ibarra-Castanedo, C., Pivar\u010diov\u00e1, E., Gargiulo, G., Maldague, X., and Arias, P. (2021). Introduction of deep learning in thermographic monitoring of cultural heritage and improvement by automatic thermogram pre-processing algorithms. Sensors, 21.","DOI":"10.3390\/s21030750"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wei, Z., Fernandes, H., Herrmann, H.-G., Tarpani, J.R., and Osman, A. (2021). A Deep learning method for the impact damage segmentation of curve-shaped CFRP specimens inspected by infrared thermography. Sensors, 21.","DOI":"10.3390\/s21020395"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Garrido, I., Lag\u00fcela, S., Fang, Q., and Arias, P. (2022). Introduction of the combination of thermal fundamentals and deep learning for the automatic thermographic inspection of thermal bridges and water-related problems in infrastructures. Quant. Infrared Thermogr. J., 1\u201325.","DOI":"10.1080\/17686733.2022.2060545"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/17686733.2021.1918514","article-title":"Deep convolutional neural networks for classifying breast cancer using infrared thermography","volume":"19","author":"Guevara","year":"2022","journal-title":"Quant. Infrared Thermogr. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s10765-015-1962-8","article-title":"How to Retrieve Information Inherent to Old Restorations Made on Frescoes of Particular Artistic Value Using Infrared Vision?","volume":"36","author":"Sfarra","year":"2015","journal-title":"Int. J. Thermophys."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","article-title":"A scaled conjugate gradient algorithm for fast supervised learning","volume":"6","year":"1993","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24553-9"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9361\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:31:51Z","timestamp":1760146311000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":32,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239361"],"URL":"https:\/\/doi.org\/10.3390\/s22239361","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]}}}