Texture defect detection by using polynomial interpolation and multilayer perceptron

This article deals with fabric defect detection. The quality control in textile manufacturing industry becomes an important task, and the investment in this field is more than economical when reduction in labor cost and associated benefits are considered. This work is developed in collaboration with "PARTNER TEXTILE" company which expressed its need to install automated defect fabric detection system around its circular knitting machines. In this article, we present a new fabric defect detection method based on a polynomial interpolation of the fabric texture. The different image areas with and without defects are approximated by appropriate interpolating polynomials. Then, the coefficients of these polynomials are used to train a neural network to detect and locate regions of defects. The efficiency of the method is shown through simulations on different kinds of fabric defects provided by the company and the evaluation of the classification accuracy. Comparison results show that the proposed method outperforms several existing ones in terms of rapidity, localization, and precision.