ARTDET: Machine learning software for automated detection of art deterioration in easel paintings

ARTDET: Machine learning software for automated detection of art deterioration in easel paintings

The increasing interest in digital preservation of cultural heritage has led to ARTDET, a machine learning software for automated detection of deterioration in easel paintings. This web application uses a pre-trained Mask R-CNN model to detect Lacune (areas of missing paint, resulting in visible sup...

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Bibliographic Details
Main Authors: Francisco M. Garcia-Moreno, Jesús Cortés Alcaraz, José Manuel del Castillo de la Fuente, Luis Rodrigo Rodríguez-Simón, María Visitación Hurtado-Torres
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:SoftwareX
Subjects:
Art conservation
Machine learning
Deterioration detection
Cultural heritage
Online Access:http://www.sciencedirect.com/science/article/pii/S2352711024002875
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Summary:The increasing interest in digital preservation of cultural heritage has led to ARTDET, a machine learning software for automated detection of deterioration in easel paintings. This web application uses a pre-trained Mask R-CNN model to detect Lacune (areas of missing paint, resulting in visible support panel) from the loss of the Painting Layer (LPL) and stucco repairs. ARTDET leverages high-resolution images annotated by expert restorers. The software achieved 80.4 % recall for LPL and stucco, with a 99 % confidence score in detected damages. Available as open access resource, ARTDET aids conservators and researchers in preserving invaluable artworks.
ISSN:2352-7110

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