Volume 6, Issue 2
The High-speed Fabric Defect Detection Algorithm Based on the Image Layered Model

Pengfei Li, Yang Jiao, Junfeng Jing & Jiangnan Li

Journal of Fiber Bioengineering & Informatics, 6 (2013), pp. 161-173.

Published online: 2013-06

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  • Abstract

The high-speed fabric defect detection algorithm based on fabric image layered model is proposed to achieve the goal of accurate defect detection in the fabric production process. The image layered model assumes that fabric image is a superposition of the periodic texture background image, noise image, and defect image. Thus fabrics can be divided and conquered. Firstly, image preprocessing and mean sampling algorithms were used to suppress the background texture and interference image layer, and variances sampling was used for enhancing defect image layer. Secondly, the Otsu method was applied for determining the segmentation threshold to segment the defect image automatically, then clear and accurate defect image was obtained via image post-treatment algorithm. Finally, defect positions were marked by a labeling algorithm to prepare for subsequent offine processing. Experiments on common defect images from a standard defect library were described, and experimental results show that the proposed algorithm based on image layered model is reliable, accurate, real-time and well used in the industrial field.

  • Keywords

Fabric Defect Detection Image Layered Model Otsu Method

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COPYRIGHT: © Global Science Press

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@Article{JFBI-6-161, author = {}, title = {The High-speed Fabric Defect Detection Algorithm Based on the Image Layered Model}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2013}, volume = {6}, number = {2}, pages = {161--173}, abstract = {The high-speed fabric defect detection algorithm based on fabric image layered model is proposed to achieve the goal of accurate defect detection in the fabric production process. The image layered model assumes that fabric image is a superposition of the periodic texture background image, noise image, and defect image. Thus fabrics can be divided and conquered. Firstly, image preprocessing and mean sampling algorithms were used to suppress the background texture and interference image layer, and variances sampling was used for enhancing defect image layer. Secondly, the Otsu method was applied for determining the segmentation threshold to segment the defect image automatically, then clear and accurate defect image was obtained via image post-treatment algorithm. Finally, defect positions were marked by a labeling algorithm to prepare for subsequent offine processing. Experiments on common defect images from a standard defect library were described, and experimental results show that the proposed algorithm based on image layered model is reliable, accurate, real-time and well used in the industrial field.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi06201305}, url = {http://global-sci.org/intro/article_detail/jfbi/4831.html} }
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