Garcia-Moreno Francisco M, Fuente Jose Manuel Del Castillo de la, Rodríguez-Simón Luis Rodrigo, Hurtado-Torres María Visitación
Department of Software Engineering, Computer Science School, University of Granada, C/Periodista Daniel Saucedo Aranda, 18014, Spain.
Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, 18014, Spain.
Data Brief. 2025 Jun 25;61:111811. doi: 10.1016/j.dib.2025.111811. eCollection 2025 Aug.
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.
ArtInsight是一个创新的数据集，旨在检测美术作品，特别是油画的损坏情况。该数据集包括在格拉纳达大学使用配备105毫米镜头、ISO 125、F5光圈和1/13快门速度的数码相机拍摄的高分辨率图像，并进行了色彩校准处理。数据集有两种类型的图像：展示灰泥技术干预的图像和因绘画层损失（LPL）而出现孔洞的图像。使用VGG图像注释工具进行人工损伤标注，注释以JSON格式导出，并针对灰泥和LPL损伤进行了标注。该数据集包含14张图像，有2909个不同的损伤区域，分为训练和验证数据集。该数据集使用Python 3.7开发，并在预训练的Mask-RCNN模型上进行了微调，在损伤检测方面显示出较高的准确率（98-100%）。ArtInsight旨在促进自动损伤检测，并推动艺术保护和修复领域的未来研究。该数据集可在10.5281/zenodo.8429814上公开获取。
