Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method

Abstract

This paper proposed an approach, which is based on multi-scale wavelet transform and Gaussian mixture model, to solve the problem about automated fabric defect detection and improve the quality of fabric in the production. Firstly, the sample image was tackled by the “Pyramid” wavelet decomposition algorithm, and the new images were obtained by reconstructing with the produced wavelet coefficients using wavelet thresholding denoising method. Secondly, the obtained new images were segmented by applying the Gaussian mixture model that was based on the Expectation–Maximization (EM) algorithm. Various fabric samples were used in the evaluation, and the experimental results showed that the designed algorithm could precisely locate the position of defect and segment the defect.

Keywords:

• defect detection
• multi-scale wavelet transform
• Gaussian mixture model
• EM algorithm

Acknowledgements

We would like to thank the Natural Science Foundation of China (61301276), Shaanxi Provincial Education Department (Program No. 2013JK1084), China National Textile And Apparel Council (Program No. 2013066) and Undergraduate Training Programs for Innovation and Entrepreneurship of Xi’an Polytechnic University (Program No.201303012).