Fabric defect detection using one-class classifier

Abstract

Textile is wide, very important in critical industry, because it provide lot prodcut to the human day to day life. As example cloths, wipes, transportation materials, wipes, hosuning materials etc. Then quality of the products are very important for their demand. Therefore defect identification during the production is very importat and then they can maintain better price for their production. Therefore fabric defect detection and identification is a very impotant part of the textile industry's quality control process. Currently, there are many manualinspection method to identify defects and, to enhance the efficiency, it is needed to repace manual inspectionmethod bby a automatic inspection method. Machine vision is diversifying and expanding in defect detection using deep learning. Traditional systems like detecting and classifying defects using image segmentation, defect detection and image classification have some limitations like requiring a lot of defective data to the training process and needing pre-identification of defects in the datasets. However, it is very difficult to get a large amount of actual data with defects and real-time processes. The one-class classifier is a classical machine learning problem that has received considerable attention recently for fabric defect detection. Tin this scenario, only non-defective class data are available in the training process and avoid the requirement of defective to train process. However, State of art models in deep neural networks with one-class classifiers is still unable to record higher accuracy. This research proposes our approach, for identifying defective fabric using features of the non-defective fabric with higher accuracy. The implications of this research can be an initiative to such applications. That approach consists of a VGG-16 pre-trained framework and trainable network with a new Loss function for increase accuracy of defect detection.