ArtInsight: A detailed dataset for detecting deterioration in easel paintings

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-06-25 DOI:10.1016/j.dib.2025.111811

Francisco M. Garcia-Moreno , Jose Manuel del Castillo de la Fuente , Luis Rodrigo Rodríguez-Simón , María Visitación Hurtado-Torres

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引用次数: 0

Abstract

ArtInsight is an innovative dataset designed to detect deterioration 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 calibration. 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 Annotator was employed for manual damage labeling, with annotations exported in JSON format and labeled for stucco and LPL damages. The dataset comprises 14 images with 2909 distinct 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 facilitate automated damage detection and foster future research in art conservation and restoration. The dataset is publicly available at 10.5281/zenodo.8429814.

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ArtInsight：用于检测架上画作劣化的详细数据集

ArtInsight是一个创新的数据集，旨在检测美术，特别是架上绘画的恶化。该数据集包括在格拉纳达大学使用105毫米镜头的数码相机拍摄的高分辨率图像，ISO 125, F5，快门速度为1/13，并进行了色彩校准处理。两种类型的图像具有特色：显示灰泥技术干预的图像和由于油漆层（LPL）丢失而导致的Lacune图像。使用VGG Image Annotator进行人工损伤标注，以JSON格式导出标注，对灰泥和LPL损伤进行标注。该数据集包括14幅图像，包含2909个不同的损伤区域，分为训练和验证数据集。使用Python 3.7开发并在预训练的Mask-RCNN模型上进行微调，该数据集在损伤检测方面显示出很高的准确率（98 - 100%）。ArtInsight旨在促进自动损伤检测，并促进艺术品保护和修复的未来研究。该数据集可在10.5281/zenodo.8429814公开获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.