DefectAtlas-20:用于接缝材料和表面状况分类的真实世界图像数据集。

IF 1.9 Q3 MULTIDISCIPLINARY SCIENCES
Data in Brief Pub Date : 2026-08-27 eCollection Date: 2026-08-01 DOI:10.1016/j.dib.2026.113215
Ricardo Buettner, Leopold Fischer-Brandies, Lukas Pasold, Luca Eisentraut
{"title":"DefectAtlas-20:用于接缝材料和表面状况分类的真实世界图像数据集。","authors":"Ricardo Buettner, Leopold Fischer-Brandies, Lukas Pasold, Luca Eisentraut","doi":"10.1016/j.dib.2026.113215","DOIUrl":null,"url":null,"abstract":"<p><p>Reliable automated surface inspection requires image datasets that reflect the conditions under which material damage occurs in practice. However, many existing defect datasets focus on individual materials, specific components, or controlled imaging environments, limiting their suitability for studying material-dependent surface defect appearance and their performance robustness under real-world variation. This article presents DefectAtlas-20, a dataset of 13,233 RGB photographs organized into 20 classes formed by combining ten commonly encountered material categories with defect and no-defect surface conditions. The materials comprise uncoated and coated metal, uncoated and coated wood, rubber, textile, cardboard, glass, cable, and hard plastic. Images were acquired using consumer mobile devices in heterogeneous indoor and outdoor field environments around Hamburg, Germany. They capture variation in illumination, viewing angle, background, object geometry, surface texture, reflectance, and usage condition. The defect classes contain naturally occurring damage, while the corresponding no-defect classes show visually comparable surfaces without visible defects. All images were collected according to a predefined protocol, manually labeled, independently reviewed, centrally quality-controlled, anonymized through metadata removal, and converted to lossless PNG format in the sRGB color space. The released data package includes 512 × 512-pixel images, class and acquisition metadata, and documentation of the collection and curation process. The dataset provides a benchmark for 20-class material-condition classification and additionally supports binary defect recognition, material classification, material-specific defect analysis, transfer learning, and domain-generalization research. Its material diversity and real-world acquisition conditions facilitate the development and evaluation of computer-vision methods for surface inspection, maintenance, refurbishment, and damage assessment.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"113215"},"PeriodicalIF":1.9000,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13571519/pdf/","citationCount":"0","resultStr":"{\"title\":\"DefectAtlas-20: A real-world image dataset for joint material and surface-condition classification.\",\"authors\":\"Ricardo Buettner, Leopold Fischer-Brandies, Lukas Pasold, Luca Eisentraut\",\"doi\":\"10.1016/j.dib.2026.113215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Reliable automated surface inspection requires image datasets that reflect the conditions under which material damage occurs in practice. However, many existing defect datasets focus on individual materials, specific components, or controlled imaging environments, limiting their suitability for studying material-dependent surface defect appearance and their performance robustness under real-world variation. This article presents DefectAtlas-20, a dataset of 13,233 RGB photographs organized into 20 classes formed by combining ten commonly encountered material categories with defect and no-defect surface conditions. The materials comprise uncoated and coated metal, uncoated and coated wood, rubber, textile, cardboard, glass, cable, and hard plastic. Images were acquired using consumer mobile devices in heterogeneous indoor and outdoor field environments around Hamburg, Germany. They capture variation in illumination, viewing angle, background, object geometry, surface texture, reflectance, and usage condition. The defect classes contain naturally occurring damage, while the corresponding no-defect classes show visually comparable surfaces without visible defects. All images were collected according to a predefined protocol, manually labeled, independently reviewed, centrally quality-controlled, anonymized through metadata removal, and converted to lossless PNG format in the sRGB color space. The released data package includes 512 × 512-pixel images, class and acquisition metadata, and documentation of the collection and curation process. The dataset provides a benchmark for 20-class material-condition classification and additionally supports binary defect recognition, material classification, material-specific defect analysis, transfer learning, and domain-generalization research. Its material diversity and real-world acquisition conditions facilitate the development and evaluation of computer-vision methods for surface inspection, maintenance, refurbishment, and damage assessment.</p>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":\"68 \",\"pages\":\"113215\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2026-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13571519/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.dib.2026.113215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2026/8/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.dib.2026.113215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/8/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

可靠的自动化表面检测需要能够反映实际材料损坏发生条件的图像数据集。然而,许多现有的缺陷数据集中于单个材料、特定组件或受控成像环境,限制了它们在研究材料相关表面缺陷外观及其在现实世界变化下的性能稳健性方面的适用性。本文介绍了DefectAtlas-20,这是一个包含13233张RGB照片的数据集,通过结合10种常见的具有缺陷和无缺陷表面条件的材料类别,将其分为20类。这些材料包括未涂层和涂层的金属、未涂层和涂层的木材、橡胶、纺织品、纸板、玻璃、电缆和硬塑料。使用消费者移动设备在德国汉堡周围不同的室内和室外野外环境中获取图像。它们捕捉光照、视角、背景、物体几何、表面纹理、反射率和使用条件的变化。缺陷类包含自然发生的损坏,而相应的无缺陷类显示没有可见缺陷的视觉上可比较的表面。所有图像都是按照预定义的协议收集的,手动标记,独立审查,集中质量控制,通过去除元数据进行匿名化,并在sRGB色彩空间中转换为无损PNG格式。发布的数据包包括512 × 512像素的图像,类和采集元数据,以及收集和策展过程的文档。该数据集为20类材料状态分类提供了基准,并支持二元缺陷识别、材料分类、材料特定缺陷分析、迁移学习和领域泛化研究。它的材料多样性和现实世界的获取条件促进了计算机视觉方法的发展和评估,用于表面检查、维护、翻新和损坏评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DefectAtlas-20: A real-world image dataset for joint material and surface-condition classification.

Reliable automated surface inspection requires image datasets that reflect the conditions under which material damage occurs in practice. However, many existing defect datasets focus on individual materials, specific components, or controlled imaging environments, limiting their suitability for studying material-dependent surface defect appearance and their performance robustness under real-world variation. This article presents DefectAtlas-20, a dataset of 13,233 RGB photographs organized into 20 classes formed by combining ten commonly encountered material categories with defect and no-defect surface conditions. The materials comprise uncoated and coated metal, uncoated and coated wood, rubber, textile, cardboard, glass, cable, and hard plastic. Images were acquired using consumer mobile devices in heterogeneous indoor and outdoor field environments around Hamburg, Germany. They capture variation in illumination, viewing angle, background, object geometry, surface texture, reflectance, and usage condition. The defect classes contain naturally occurring damage, while the corresponding no-defect classes show visually comparable surfaces without visible defects. All images were collected according to a predefined protocol, manually labeled, independently reviewed, centrally quality-controlled, anonymized through metadata removal, and converted to lossless PNG format in the sRGB color space. The released data package includes 512 × 512-pixel images, class and acquisition metadata, and documentation of the collection and curation process. The dataset provides a benchmark for 20-class material-condition classification and additionally supports binary defect recognition, material classification, material-specific defect analysis, transfer learning, and domain-generalization research. Its material diversity and real-world acquisition conditions facilitate the development and evaluation of computer-vision methods for surface inspection, maintenance, refurbishment, and damage assessment.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Data in Brief
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书