Segmentation of defects in textile fabric with robust texture representation and total variation
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 1 May 2020
Issue publication date: 2 November 2020
Abstract
Purpose
Visual quality control on raw textile fabrics is a vital process in weaving factories to ensure their exterior quality (visual defects or imperfection) satisfying customer requirements. Commonly, this critical process is manually conducted by human inspectors, which can hardly provide a fast and reliable inspection results due to fatigue and subjective errors. To meet modern production needs, it is highly demanded to develop an automated defect inspection system by replacing human eyes with computer vision.
Design/methodology/approach
As a structural texture, fabric textures can be effectively represented by a linearly summation of basic elements (dictionary). To create a robust representation of a fabric texture in an unsupervised manner, a smooth constraint is imposed on dictionary learning model. Such representation is robust to defects when using it to recover a defective image. Thus an abnormal map (likelihood of defective regions) can be computed by measuring similarity between recovered version and itself. Finally, the total variation (TV) based model is built to segment defects on the abnormal map.
Findings
Different from traditional dictionary learning method, a smooth constraint is introduced in dictionary learning that not only able to create a robust representation for fabric textures but also avoid the selection of dictionary size. In addition, a TV based model is designed according to defects' characteristics. The experimental results demonstrate that (1) the dictionary with smooth constraint can generate a more robust representation of fabric textures compared to traditional dictionary; (2) the TV based model can achieve a robust and good segmentation result.
Originality/value
The major originality of the proposed method are: (1) Dictionary size can be set as a constant instead of selecting it empirically; (2) The total variation based model is built, which can enhance less salient defects, improving segmentation performance significantly.
Keywords
Acknowledgements
“This research was supported by the National Natural Science Foundation of China [grant number 61501209]”.
Citation
Zhou, J. and Liu, J. (2020), "Segmentation of defects in textile fabric with robust texture representation and total variation", International Journal of Clothing Science and Technology, Vol. 32 No. 6, pp. 813-823. https://doi.org/10.1108/IJCST-10-2019-0157
Publisher
:Emerald Publishing Limited
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