@misc{10481/106118,
year = {2024},
month = {12},
url = {https://hdl.handle.net/10481/106118},
abstract = {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.},
organization = {MICIU/AEI/ 10.13039/501100011033 PID2023-149185OB-I00},
organization = {ERDF/EU},
organization = {Research Group Modelling & Development of Advanced Software Systems (TIC-230)},
publisher = {Elsevier},
keywords = {Art conservation},
keywords = {Machine learning},
keywords = {Deterioration detection},
keywords = {Cultural heritage},
title = {ARTDET: Machine learning software for automated detection of art deterioration in easel paintings},
doi = {10.1016/j.softx.2024.101917},
author = {García Moreno, Francisco Manuel and Cortés Alcaráz, Jesús and Del Castillo de la Fuente, José Manuel and Rodríguez Simón, Luis Rodrigo and Hurtado Torres, María Visitación},
}