Work

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

Year: 2024

Type: software-paper

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.

Source: SoftwareX

DOI: 10.1016/j.softx.2024.101917

Authors: Francisco M. Garcia-Moreno, Jesús Cortés Alcaraz, José Manuel del Castillo de la Fuente, Luis Rodrigo Rodríguez-Simón, María Visitación Hurtado-Torres

Institution: Universidad de Granada

Language: English

Topic: Image Processing and 3D Reconstruction

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $970

Funders: Ministerio de Ciencia, Innovación y Universidades, Universidad de Granada, Agencia Estatal de Investigación

OpenAlex ID: W4403157336

Cites: 10

Cited by: 10

Related to: 10