CottonFabricImageBD: An image dataset characterized by the percentage of cotton in a fabric for computer vision-based garment recycling

The utilization of computer vision techniques has significantly enhanced the automation processes across various industries, including textile manufacturing, agriculture, and information technology. Specifically, in the domain of textile manufacturing, these techniques have revolutionized the detect...

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Bibliographic Details
Published in:	Data in brief Vol. 55; p. 110712
Main Authors:	Niloy, Nishat Tasnim, Ahmed, Md. Rayhan, Ananna, Sinthia Sarkar, Kater, Sanjida, Shorna, Iffat Jahan, Sneha, Sadika Islam, Ferdaus, Md. Hasanul, Islam, Mohammad Manzurul, Rashid, Mohammad Rifat Ahmmad, Jabid, Taskeed, Ali, Md. Sawkat
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier Inc 01-08-2024 Elsevier
Subjects:	Artificial intelligence Computer vision Data Image classification Textile engineering Computer vision Textile engineering Artificial intelligence Image classification
Online Access:	Get full text
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Summary:	The utilization of computer vision techniques has significantly enhanced the automation processes across various industries, including textile manufacturing, agriculture, and information technology. Specifically, in the domain of textile manufacturing, these techniques have revolutionized the detection of fiber defects and the quantification of cotton content in fabrics. Traditionally, the assessment of cotton percentages was a labor-intensive and time-consuming process that relied heavily on manual testing methods. However, the adoption of computer vision approaches requires a comprehensive dataset of fabric samples, each with a known cotton percentage, to serve as training data for machine learning models. This paper introduces a novel dataset comprising 1300 original images, covering a wide range of cotton percentages across thirteen distinct categories, from 30% to 99%. By employing image augmentation techniques, such as- rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming, this dataset has been expanded to include a total of 27,300 images, thereby enhancing its utility for training and validating computer vision models aimed at accurately determining cotton content in fabrics. Through the extraction of pertinent features from the images of fabrics, this dataset holds the potential to significantly improve the accuracy and efficiency of computer vision-based cotton percentage detection.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2352-3409 2352-3409
DOI:	10.1016/j.dib.2024.110712