RT info:eu-repo/semantics/article
T1 Dataset for defect detection in textile manufacturing
A1 Gil Arroyo, Beatriz
A1 Marcos Sanz, Juan
A1 Arroyo Puente, Ángel
A1 Urda Muñoz, Daniel
A1 Basurto Hornillos, Nuño
A1 Herrero Cosío, Álvaro
K1 Textile manufacturing
K1 Textile industry
K1 Batavia and Sarga fabric
K1 Defect detection
K1 Image analysis
K1 Artificial vision
K1 Quality inspection
K1 Inteligencia artificial
K1 Artificial intelligence
K1 Industria textil
K1 Textile industry
AB 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.
PB Elsevier
SN 2352-3409
YR 2025
FD 2025-04
LK https://hdl.handle.net/10259/10545
UL https://hdl.handle.net/10259/10545
LA eng
NO Artículo de datos
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 29-abr-2026