| Autor: |
Francisco M. Garcia-Moreno, Jesús Cortés Alcaraz, José Manuel del Castillo de la Fuente, Luis Rodrigo Rodríguez-Simón, María Visitación Hurtado-Torres |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
SoftwareX, Vol 28, Iss , Pp 101917- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2024.101917 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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