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Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm

Pandia Rajan Jeyaraj (Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, India)
Edward Rajan Samuel Nadar (Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, India)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 5 August 2019

Abstract

Purpose

The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm.

Design/methodology/approach

To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification.

Findings

The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate.

Practical implications

The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm.

Originality/value

The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.

Keywords

Acknowledgements

In this paper, there is no potential conflict of interests. The authors would like to thank the management, Principal of Mepco Schlenk Engineering College, Sivakasi for providing the authors with necessary facilities to carry out this research work, Mepco NI LabVIEW academy for providing necessary hardware support and Micro fine fabrics for providing testing fabrics used in this research work.

Citation

Jeyaraj, P.R. and Samuel Nadar, E.R. (2019), "Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm", International Journal of Clothing Science and Technology, Vol. 31 No. 4, pp. 510-521. https://doi.org/10.1108/IJCST-11-2018-0135

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited