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Fabric defect detection based on low-rank decomposition with structural constraints

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Abstract

Fabric defect detection is an important part of the fabric production process. To realize the automatic detection of fabric defects, many algorithms based on machine vision technology have been proposed. However, the defect detection algorithms for patterned fabrics are still not mature enough. This paper proposes a fabric defect detection method based on low-rank decomposition with structural constraints. This method extracts the energy features and then constructs a fusion image of the original image and the energy image to highlight the defective regions. By considering the spatial connection of defective pixels, a new low-rank decomposition model is constructed by introducing the structured sparsity-inducing norm. After the low-rank decomposition, we can get the sparse part containing the defective pixels with high spatial continuity. Finally, we obtain the defect detection result by thresholding the sparse part. Experimental comparisons show that our method is superior to several state-of-the-art fabric defect detection methods.

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Acknowledgments

This research was supported by Tianjin Science and Technology Plan Project (Grant No. 18JCTPJC62700). And the database employed in this research is provided by Industrial Automation Research Laboratory from Department of Electrical and Electronic Engineering of Hong Kong University. Link address of the dataset: https://ytngan.wordpress.com/codes.

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Correspondence to Guohua Liu.

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Guohua Liu declares that he has no conflict of interest. Fei Li declares that he has no conflict of interest.

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Liu, G., Li, F. Fabric defect detection based on low-rank decomposition with structural constraints. Vis Comput 38, 639–653 (2022). https://doi.org/10.1007/s00371-020-02040-y

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