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 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.
Authors
- Francisco M. García-Moreno (ORCID: https://orcid.org/0000-0001-6366-3758)
- María Visitación Hurtado (ORCID: https://orcid.org/0000-0001-6345-6062)
- José Manuel del Castillo de la Fuente (ORCID: https://orcid.org/0000-0002-7066-3542)
- Jesús Cortés Alcaraz
- Luis Rodrigo Rodríguez-Simón
Institutions
- Universidad de Granada (ES)
Publication Details
- Journal
- SoftwareX
- Published
- 2024-10-05
- DOI
- https://doi.org/10.1016/j.softx.2024.101917
- Citations
- 6
- Primary Topic
- Aesthetic Perception and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.06
Funders
- Universidad de Granada
- Agencia Estatal de Investigación