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

• Published in: International journal of clothing science and technology Vol. 31; no. 4; pp. 510 - 521
• Main Authors: Jeyaraj, Pandia Rajan, Samuel Nadar, Edward Rajan
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 05.08.2019; Emerald Group Publishing Limited
• Subjects: Accuracy; Algorithms; Artificial neural networks; Automation; CAD; Classification; Computer aided design; Computer vision; Computing time; Datasets; Deep learning; Defects; Design defects; Machine learning; Performance evaluation; Performance indices; Scanners; Sensitivity analysis; Standard data; Success; Textiles; Computer vision; Fabric defect detection; Feature extraction; Defect classifications; Deep learning algorithm
• ISSN: 0955-6222; 1758-5953
• DOI: 10.1108/IJCST-11-2018-0135

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.