@misc{10481/106117,
year = {2025},
month = {8},
url = {https://hdl.handle.net/10481/106117},
abstract = {ArtInsight is an innovative dataset designed to detect dete- rioration in fine art, specifically easel paintings. The dataset includes high-resolution images captured at the University of Granada using a digital camera with a 105 mm lens, ISO 125, F5, and a shutter speed of 1/13, and processed for color cal- ibration. Two types of images are featured: those showing stucco technique interventions and those with Lacune from the loss of the Painting Layer (LPL). The VGG Image Annota- tor was employed for manual damage labeling, with annota- tions exported in JSON format and labeled for stucco and LPL damages. The dataset comprises 14 images with 2909 dis- tinct damage areas, split into training and validation datasets. Developed using Python 3.7 and fine-tuned on a pre-trained Mask-RCNN model, this dataset demonstrates high accuracy rates (98–100 %) in damage detection. ArtInsight aims to fa- cilitate automated damage detection and foster future research in art conservation and restoration.},
organization = {Department of Software Engineering, Computer Science School, University of Granada,},
publisher = {Elsevier},
keywords = {Art restoration},
keywords = {Damage detection},
keywords = {Easel paintings},
keywords = {Deep learning},
keywords = {Mask-RCNN},
keywords = {Image annotation},
keywords = {Art dataset},
keywords = {Digital preservation},
title = {ArtInsight: A detailed dataset for detecting deterioration in easel paintings},
doi = {10.1016/j.dib.2025.111811},
author = {García Moreno, Francisco Manuel and Del Castillo de la Fuente, José Manuel and Rodríguez Simón, Luis Rodrigo and Hurtado Torres, María Visitación},
}