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Abstract
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.
Highlights
As the global market puts higher and higher demand on product quality, automated inspection has increasingly developed in recent years in all areas such as manufacture of mechanical parts and electronic boards.[1,2,3,4,5,6]Defect detection is one of the main steps in quality control in industry
We develop a new defect detection method based on polynomial interpolation of a set of a pixel and its neighborhood to extract feature database to be used to train a multilayer perceptron (MLP)
We proposed a new method for defect detection in fabrics
Summary
As the global market puts higher and higher demand on product quality, automated inspection has increasingly developed in recent years in all areas such as manufacture of mechanical parts and electronic boards.[1,2,3,4,5,6]Defect detection is one of the main steps in quality control in industry. We develop a new defect detection method based on polynomial interpolation of a set of a pixel and its neighborhood to extract feature database to be used to train a multilayer perceptron (MLP). Different texture features for regions with and without defects are presented to the neural network.
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