The ARTDET project, led by Francisco M. García-Moreno—member of the MYDASS Research Group, has been featured by RTVE in a news piece broadcast on Telediario 2. The report presents how artificial intelligence can support the work of conservators and restorers by helping to identify deterioration in easel paintings.
This research line brings together computer science, artificial intelligence, image analysis, and cultural heritage conservation to explore new digital tools for the documentation and study of damage in pictorial works.
Artificial Intelligence Applied to Painting Conservation
ARTDET focuses on the automated detection of deterioration in easel paintings. Using machine learning and computer vision techniques, the system can help identify areas affected by damage, such as paint layer loss or restoration-related materials such as stucco. These results are intended to serve as a complementary resource for specialists, supporting the documentation process while keeping expert supervision at the center of conservation and restoration practice.
As principal investigator of this research line, Francisco M. García-Moreno highlights that the aim of ARTDET is not to replace the expert eye of conservators and restorers, but to provide digital tools that can assist them in repetitive and time-consuming tasks. By combining high-resolution image analysis, expert annotations, and artificial intelligence models, the project seeks to contribute to more efficient, transparent, and reproducible methods for studying deterioration in cultural heritage assets.
From Scientific Research to Public Outreach
The appearance of ARTDET on RTVE, Spain’s national public broadcaster, represents an important step in bringing university research closer to society. The news piece shows a concrete example of how artificial intelligence can be applied beyond purely technological fields, with potential impact in art conservation, heritage documentation, and restoration processes.
This dissemination activity is directly related to two recent scientific outputs: the ARTDET software, published in SoftwareX, and the ArtInsight dataset, published in Data in Brief. Together, both works provide an open and reproducible basis for the development of AI-based tools aimed at detecting and documenting deterioration in easel paintings.
RTVE Coverage
RTVE News article:
La inteligencia artificial entra en el taller de restauración
https://www.rtve.es/noticias/20260622/inteligencia-artificial-entra-taller-restauracion/17083502.shtml
RTVE Play / Telediario 2 video:
La inteligencia artificial entra en el taller de restauración
https://www.rtve.es/play/videos/telediario-2/inteligencia-artificial-entra-taller-restauracion/17127006/
References
García-Moreno, F. M., del Castillo de la Fuente, J. M., Rodríguez-Simón, L. R., & Hurtado-Torres, M. V. (2025). ArtInsight: A detailed dataset for detecting deterioration in easel paintings. Data in Brief, 61, 111811.
DOI: https://doi.org/10.1016/j.dib.2025.111811
García-Moreno, F. M., Cortés Alcaraz, J., del Castillo de la Fuente, J. M., Rodríguez-Simón, L. R., & Hurtado-Torres, M. V. (2024). ARTDET: Machine Learning Software for Automated Detection of Art Deterioration in Easel Paintings. SoftwareX, 28, 101917.
DOI: https://doi.org/10.1016/j.softx.2024.101917
