A Dataset of Raw Fabric Grayscale Images for Defect Detection

[EN] This article presents RAW-FABRID (RAW FABric Image Dataset), a publicly available annotated dataset for raw fabric defect detection using computer vision techniques. It addresses a major limitation in textile inspection, where reliance on private datasets hinders objective methodological compar...

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Autores: Pérez-Llorens, Ruben|||0000-0003-4533-7064, Albero-Albero, Teresa|||0000-0002-5162-6092, Silvestre-Blanes, Javier|||0000-0001-7091-0040
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::e08cc5afa30615830de9d032e3b76ad4
Acceso en línea:https://riunet.upv.es/handle/10251/235859
Access Level:acceso abierto
Palabra clave:Fabric defect detection
Textile manufacturing
Textile industry
Image analysis
Artificial vision
Quality inspection
Computer vision
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spelling A Dataset of Raw Fabric Grayscale Images for Defect DetectionPérez-Llorens, Ruben|||0000-0003-4533-7064Albero-Albero, Teresa|||0000-0002-5162-6092Silvestre-Blanes, Javier|||0000-0001-7091-0040Fabric defect detectionTextile manufacturingTextile industryImage analysisArtificial visionQuality inspectionComputer vision[EN] This article presents RAW-FABRID (RAW FABric Image Dataset), a publicly available annotated dataset for raw fabric defect detection using computer vision techniques. It addresses a major limitation in textile inspection, where reliance on private datasets hinders objective methodological comparisons. RAW-FABRID was acquired using a custom-built inspection machine equipped with controlled LED illumination and a line-scan camera. The dataset includes grayscale fabric images collected from several manufacturers to ensure variability in textures and patterns. It comprises 709 high-resolution images (1792 x 1024 pixels), including both defect-free and defective samples. To maximize reusability, data are provided in two complementary formats: high-resolution images (cropped to remove peripheral acquisition artifacts) for global analysis, and a patch-based organization following the widely adopted MVTec Anomaly Detection benchmark structure. The latter divides images into 256 x 256 pixel patches for direct machine learning integration. Crucially, the dataset is accompanied by comprehensive metadata (CSV) and precise COCO-formatted annotations (JSON) for both subsets, ensuring full traceability and supporting object detection and semantic segmentation. The dataset is publicly available through Mendeley Data, enabling reproducible research and objective benchmarking of defect detection algorithms.This research was funded by the Agencia Valenciana de la Innovacio (AVI) through the project "System for Defect Detection and Classification Using Artificial Vision Based on Deep Learning", under the program Valorization and Transfer of Research Results to Companies, call 2022-2024, grant number INNVA1/2022/20. The project was co-financed by the Instituto Valenciano de Competitividad e Innovacion (IVACE) and the European Union. The APC was funded by the aforementioned project INNVA1/2022/20.MDPI AGInstituto Universitario Mixto de Tecnología de InformáticaDepartamento de Informática de Sistemas y ComputadoresEscuela Politécnica Superior de AlcoyAgència Valenciana de la InnovacióRepositorio Institucional de la Universitat Politècnica de València Riunet20262026-05-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/235859reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAGENCIA VALENCIANA DE LA INNOVACION AGENCIA VALENCIANA DE LA INNOVACION INNVA1%2F2022%2F20 Sistema de detección y clasificación de defectos en tejidos mediante visión artificial basado en deep learningopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::e08cc5afa30615830de9d032e3b76ad42026-06-13T07:49:27Z
dc.title.none.fl_str_mv A Dataset of Raw Fabric Grayscale Images for Defect Detection
title A Dataset of Raw Fabric Grayscale Images for Defect Detection
spellingShingle A Dataset of Raw Fabric Grayscale Images for Defect Detection
Pérez-Llorens, Ruben|||0000-0003-4533-7064
Fabric defect detection
Textile manufacturing
Textile industry
Image analysis
Artificial vision
Quality inspection
Computer vision
title_short A Dataset of Raw Fabric Grayscale Images for Defect Detection
title_full A Dataset of Raw Fabric Grayscale Images for Defect Detection
title_fullStr A Dataset of Raw Fabric Grayscale Images for Defect Detection
title_full_unstemmed A Dataset of Raw Fabric Grayscale Images for Defect Detection
title_sort A Dataset of Raw Fabric Grayscale Images for Defect Detection
dc.creator.none.fl_str_mv Pérez-Llorens, Ruben|||0000-0003-4533-7064
Albero-Albero, Teresa|||0000-0002-5162-6092
Silvestre-Blanes, Javier|||0000-0001-7091-0040
author Pérez-Llorens, Ruben|||0000-0003-4533-7064
author_facet Pérez-Llorens, Ruben|||0000-0003-4533-7064
Albero-Albero, Teresa|||0000-0002-5162-6092
Silvestre-Blanes, Javier|||0000-0001-7091-0040
author_role author
author2 Albero-Albero, Teresa|||0000-0002-5162-6092
Silvestre-Blanes, Javier|||0000-0001-7091-0040
author2_role author
author
dc.contributor.none.fl_str_mv Instituto Universitario Mixto de Tecnología de Informática
Departamento de Informática de Sistemas y Computadores
Escuela Politécnica Superior de Alcoy
Agència Valenciana de la Innovació
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Fabric defect detection
Textile manufacturing
Textile industry
Image analysis
Artificial vision
Quality inspection
Computer vision
topic Fabric defect detection
Textile manufacturing
Textile industry
Image analysis
Artificial vision
Quality inspection
Computer vision
description [EN] This article presents RAW-FABRID (RAW FABric Image Dataset), a publicly available annotated dataset for raw fabric defect detection using computer vision techniques. It addresses a major limitation in textile inspection, where reliance on private datasets hinders objective methodological comparisons. RAW-FABRID was acquired using a custom-built inspection machine equipped with controlled LED illumination and a line-scan camera. The dataset includes grayscale fabric images collected from several manufacturers to ensure variability in textures and patterns. It comprises 709 high-resolution images (1792 x 1024 pixels), including both defect-free and defective samples. To maximize reusability, data are provided in two complementary formats: high-resolution images (cropped to remove peripheral acquisition artifacts) for global analysis, and a patch-based organization following the widely adopted MVTec Anomaly Detection benchmark structure. The latter divides images into 256 x 256 pixel patches for direct machine learning integration. Crucially, the dataset is accompanied by comprehensive metadata (CSV) and precise COCO-formatted annotations (JSON) for both subsets, ensuring full traceability and supporting object detection and semantic segmentation. The dataset is publicly available through Mendeley Data, enabling reproducible research and objective benchmarking of defect detection algorithms.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-05-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/235859
url https://riunet.upv.es/handle/10251/235859
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv AGENCIA VALENCIANA DE LA INNOVACION AGENCIA VALENCIANA DE LA INNOVACION INNVA1%2F2022%2F20 Sistema de detección y clasificación de defectos en tejidos mediante visión artificial basado en deep learning
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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