Automated textile defect classification by Bayesian classifier based on statistical features

Abstract

Textile inspection system, which carries a lot of importance in the production process of textile goods, has been part of a great deal of research for automating the process. Manual textile inspection is a lengthy, slow as well as erroneous job; therefore, automation of textile inspection is a demand of time. Machine vision based i.e. automated fabric inspection deals with two primary challenges, namely defect detection and defect classification. The quality and quantity of research done on defect detection and classification is still not up to the mark. Our focus is on detecting various defects in textile fabrics and classifying them. We extracted features using statistical techniques. Images of textile fabrics were used as sample. Inspecting the images, geometric and statistical features were found out. Through this paper, we bring in a suitable Bayesian classifier to classify the images into different classes of defective properties. Our approach has delivered acceptable accuracy compared to other works in the domain of textile defect detection and classification.