2026-04-29T13:16:51Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/105452025-06-13T07:30:25Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Dataset for defect detection in textile manufacturing Gil Arroyo, Beatriz Marcos Sanz, Juan Arroyo Puente, Ángel Urda Muñoz, Daniel Basurto Hornillos, Nuño Herrero Cosío, Álvaro Textile manufacturing Textile industry Batavia and Sarga fabric Defect detection Image analysis Artificial vision Quality inspection Inteligencia artificial Industria textil Artificial intelligence Textile industry Artículo de datos This dataset, collected during November 2022 at Textil Santanderina, a leading textile manufacturer based in Cabezón de la Sal (Cantabria, Spain), comprises high-resolution images of Batavia and Sarga fabrics. The images were captured as part of a project to document and analyze the intricate weaves and patterns of these fabrics. Using a high-resolution camera under controlled lighting conditions, detailed images were obtained to ensure consistent quality and accurate representation of the fabric's texture and colour. The dataset is provided in processed format, where images have been downscaled from 16 bits to 8 bits, cropped, and classified into cases and controls. The primary reuse potential of this dataset lies in its application for Artificial Intelligence (AI) and Machine Learning (ML) models aimed at defect detection in textile manufacturing. By leveraging these high-quality processed images, researchers and developers can train models to identify and classify various types of fabric defects, such as weave inconsistencies, colour variations, and surface irregularities. This can significantly enhance the efficiency and accuracy of quality control processes in textile production. Additionally, the dataset serves as a valuable resource for academic research in textile engineering and material science. It can be used to study the properties and behaviours of Batavia and Sarga weaves under different conditions, contributing to advancements in fabric design and manufacturing techniques. The detailed visual information provided by the processed images also supports the development of new methodologies for automated textile inspection and quality assurance. By making this dataset available, Textil Santanderina and University of Burgos aim to support innovation and improvement in textile quality control through AI-driven solutions, fostering collaboration and development within the industry. The funding for this project was provided by the DECENT (Deep Learning for automatic Textile Inspection) initiative under the DIH-World 2nd Open Call framework. The authors express their gratitude to INADE for their collaboration in acquiring the images. 2025-06-12T09:40:00Z 2025-06-12T09:40:00Z 2025-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2352-3409 https://hdl.handle.net/10259/10545 10.1016/j.dib.2025.111451 eng Data in Brief. 2025, V. 59, 111451 https://doi.org/10.1016/j.dib.2025.111451 Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess application/pdf Elsevier