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
Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Department of Computer Science and Engineering, United International University, Bangladesh.
Data Brief. 2024 Jul 6;55:110712. doi: 10.1016/j.dib.2024.110712. eCollection 2024 Aug.
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.
计算机视觉技术的应用显著提升了包括纺织制造、农业和信息技术在内的各个行业的自动化进程。具体而言，在纺织制造领域，这些技术彻底改变了纤维缺陷检测和织物中棉花含量定量的方式。传统上，棉花百分比的评估是一项劳动密集型且耗时的过程，严重依赖人工测试方法。然而，采用计算机视觉方法需要一个包含织物样本的综合数据集，每个样本都有已知的棉花百分比，作为机器学习模型的训练数据。本文介绍了一个新颖的数据集，包含1300张原始图像，涵盖从30%到99%的13个不同类别、范围广泛的棉花百分比。通过运用图像增强技术，如旋转、水平翻转、垂直翻转、宽度偏移、高度偏移、剪切范围和缩放，该数据集已扩展至总共27300张图像，从而增强了其在训练和验证旨在准确确定织物中棉花含量的计算机视觉模型方面的效用。通过从织物图像中提取相关特征，该数据集有潜力显著提高基于计算机视觉的棉花百分比检测的准确性和效率。
