Pattern classification of fabric defects using support vector machines

Abstract

Purpose

The purpose of this paper is to address a solution to the problem of defect recognition from images using the support vector machines (SVM).

Design/methodology/approach

A SVM‐based multi‐class pattern recognition system has been developed for inspecting commonly occurring fabric defects such as neps, broken ends, broken picks and oil stain. A one‐leave‐out cross validation technique is applied to assess the accuracy of the SVM classifier in classifying fabric defects.

Findings

The investigation indicates that the fabric defects can be classified with a reasonably high degree of accuracy by the proposed method.

Originality/value

The paper outlines the theory and application of SVM classifier with reference to pattern classification problem in textiles. The SVM classifier outperforms the other techniques of machine learning systems such as artificial neural network in terms of efficiency of calculation. Therefore, SVM classifier has great potential for automatic inspection of fabric defects in industry.

Citation

Ghosh, A., Guha, T., Bhar, R.B. and Das, S. (2011), "Pattern classification of fabric defects using support vector machines", International Journal of Clothing Science and Technology, Vol. 23 No. 2/3, pp. 142-151. https://doi.org/10.1108/09556221111107333