Textile Flaw Classification by Wavelet Reconstruction and BP Neural Network

Authors:
Yean Yin;Ke Zhang;Wenbing Lu
Affiliations:
College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China 430073;College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China 430073;College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China 430073
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

This paper proposes an approach of textile flaw classification based on histogram and BP neural network. The common two types of textile flaws, namely oil stain and hole, can be extracted and classified. The method can detect flaws for two types of texture fabrics: statistical textures with isotropic patterns and structural textures with oriented patterns. For the extraction of flaw features, histograms of "hole" and "oil stain" are computed as the input of BP neural network. Some samples are selected for testing, the results show that the method can effectively detect defects and classify the types of defects with high recognition correct rate.