Automated Fabric Defect Detection and Classification: A Deep Learning Approach

NC Sandhya, Nihal Mathew Sashikumar, M Priyanka, Sebastian Maria Wenisch, Kunaraj Kumarasamy

2021 / Volume 4 / Pages 315-335
Received 26 July 2021; Accepted 28 October 2021; Published 14 December 2021

A computer-based intelligent visual inspection system plays a major role in evaluating the quality of textile fabrics and its demand is continuously increasing in the textile industry, especially when the quality of textile is to be considered. In this paper, we propose an AI-based automated fabric defect detection algorithm which utilizes pre-trained deep neural network models for classifying possible fabric defects. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The Deep Convolutional Neural Network (DCNN) and a pre-trained network, AlexNet, are used to train and classify various fabric defects. With the exiting textile dataset, a maximum classification accuracy of 92.60% is achieved in the conducted simulations. With this accuracy, the detection and classification system based on this classifier model can aid the human to find faults in the fabric manufacturing unit.

fabric defects, artificial intelligence, defect classifier, AlexNet, deep neural network