Can I read Computer Vision for Automatic Detection and Classification of Fabric Defect Employing Deep Learning Algorithm on EtoBox?

Computer Vision for Automatic Detection and Classification of Fabric Defect Employing Deep Learning Algorithm by Pandia Rajan Jeyaraj; Edward Rajan Samuel Nadar is a scholarly article available to read on EtoBox.

What is Computer Vision for Automatic Detection and Classification of Fabric Defect Employing Deep Learning Algorithm about?

## 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

Author

Pandia Rajan Jeyaraj; Edward Rajan Samuel Nadar

Publisher

Emerald

Published

2019

Language

EN