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Related Experiment Video

Updated: May 20, 2025

Fabricating Cotton Analytical Devices

Fabricating Cotton Analytical Devices

Published on: August 30, 2016

Dataset for defect detection in textile manufacturing.

Beatriz Gil-Arroyo¹, Juan Marcos Sanz², Ángel Arroyo¹

¹Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n 09006, Burgos, Spain.

|March 27, 2025

Summary

This summary is machine-generated.

This dataset features high-resolution images of Batavia and Sarga fabrics for AI-driven textile defect detection. It aids in enhancing quality control and advancing textile engineering research.

Keywords:

Artificial Vision Batavia and Sarga fabric Defect detection Image analysis Quality inspection Textile industry Textile manufacturing

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Last Updated: May 20, 2025

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Published on: January 29, 2020

Area of Science:

Textile Engineering
Material Science
Computer Vision

Background:

Textile manufacturing involves intricate weaves like Batavia and Sarga.
Accurate defect detection is crucial for quality control in textile production.
High-resolution imaging provides detailed fabric surface information.

Purpose of the Study:

To create a processed dataset of Batavia and Sarga fabric images.
To facilitate the development of Artificial Intelligence (AI) and Machine Learning (ML) models for textile defect detection.
To support academic research in textile engineering and material science.

Main Methods:

Collected high-resolution images of Batavia and Sarga fabrics at Textil Santanderina.
Processed images by downscaling, cropping, and classifying them into cases and controls.
Utilized controlled lighting conditions for consistent image quality.

Main Results:

A processed dataset of high-quality fabric images is now available.
The dataset enables training AI/ML models for identifying weave inconsistencies, color variations, and surface irregularities.
The data supports research into fabric properties and automated inspection methodologies.

Conclusions:

The dataset is a valuable resource for AI-powered textile defect detection.
It can significantly improve the efficiency and accuracy of quality control in textile manufacturing.
Facilitates advancements in fabric design, manufacturing techniques, and automated quality assurance.