Volume 8, Issue 1
Fabric Defect Classification Based on LBP and GLCM

Lei Zhang, Junfeng Jing & Hongwei Zhang

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 81-89.

Published online: 2015-08

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  • Abstract

Inevitably there will be various types of fabric defect exists in textile production line. In order to distinguish and classify the types of defects more efficiently and accurately, an algorithm which combines Local Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper for fabric defect classification. The most pivotal step of the algorithm is to extract the local and global feature values of defect images. Firstly the local feature information of the image is extracted by adopting LBP algorithm. And then the overall texture information of the image is described via GLCM algorithm. In this way, the fabric image can be fully described from global and local. Finally, the two-part feature information are structured as a whole as the input of BP Neural Network. Thus the trained BP Neural Network can be used to classify the different types of defects. Experimental results show that the algorithm has higher classification accuracy.

  • Keywords

LBP Gray-level Co-occurrence Matrix (GLCM) BP Neural Network Defect Classification

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@Article{JFBI-8-81, author = {}, title = {Fabric Defect Classification Based on LBP and GLCM}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {1}, pages = {81--89}, abstract = {Inevitably there will be various types of fabric defect exists in textile production line. In order to distinguish and classify the types of defects more efficiently and accurately, an algorithm which combines Local Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper for fabric defect classification. The most pivotal step of the algorithm is to extract the local and global feature values of defect images. Firstly the local feature information of the image is extracted by adopting LBP algorithm. And then the overall texture information of the image is described via GLCM algorithm. In this way, the fabric image can be fully described from global and local. Finally, the two-part feature information are structured as a whole as the input of BP Neural Network. Thus the trained BP Neural Network can be used to classify the different types of defects. Experimental results show that the algorithm has higher classification accuracy.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi03201508}, url = {http://global-sci.org/intro/article_detail/jfbi/4688.html} }
TY - JOUR T1 - Fabric Defect Classification Based on LBP and GLCM JO - Journal of Fiber Bioengineering and Informatics VL - 1 SP - 81 EP - 89 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbi03201508 UR - https://global-sci.org/intro/article_detail/jfbi/4688.html KW - LBP KW - Gray-level Co-occurrence Matrix (GLCM) KW - BP Neural Network KW - Defect Classification AB - Inevitably there will be various types of fabric defect exists in textile production line. In order to distinguish and classify the types of defects more efficiently and accurately, an algorithm which combines Local Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper for fabric defect classification. The most pivotal step of the algorithm is to extract the local and global feature values of defect images. Firstly the local feature information of the image is extracted by adopting LBP algorithm. And then the overall texture information of the image is described via GLCM algorithm. In this way, the fabric image can be fully described from global and local. Finally, the two-part feature information are structured as a whole as the input of BP Neural Network. Thus the trained BP Neural Network can be used to classify the different types of defects. Experimental results show that the algorithm has higher classification accuracy.
Lei Zhang, Junfeng Jing & Hongwei Zhang. (2019). Fabric Defect Classification Based on LBP and GLCM. Journal of Fiber Bioengineering and Informatics. 8 (1). 81-89. doi:10.3993/jfbi03201508
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