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Robust low-rank decomposition of multi-channel feature matrices for fabric defect detection

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Abstract

Fabric defect detection plays an important role in the quality control of textile products. Most existing defect detection techniques adopted traditional pattern recognition methods, which were lacking adaptability and presented the undesirable detection accuracy. In this paper, a fabric defect detection algorithm based on multi-channel feature matrixes extraction and joint low-rank decomposition was proposed by simulating biological visual perception mechanism. Based on the fact that the second-order gradient information is more suitable for characterizing the fabric texture, we developed a novel second-order multi-channel feature extraction method by modeling the response and distribution properties of the P-type ganglion cells in the primate retina. Upon devising a powerful descriptor, a joint low-rank decomposition method is utilized to model biological visual saliency, and decomposes the fabric images into backgrounds and salient defect objects. Experimental results demonstrate that our proposed algorithm has good self-adaptability and detection performance for plain and twill fabrics or complex patterned fabrics, and is superior to the state-of-the-art methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China ((No.61772576, No.61379113), the Key Natural Science Foundation of Henan Province(No.162300410338), Science and technology innovation talent project of Education Department of Henan Province(17HASTIT019), The Henan Science Fund for Distinguished Young Scholars (184100510002).

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Authors and Affiliations

  1. School of Electric and Information Engineering, Zhongyuan University of Technology, Zhengzhou, Henan, 450007, China

    Chunlei Li, Chaodie Liu, Guangshuai Gao & Zhoufeng Liu

  2. Biomedical Engineering Department, Tulane University, New Orleans, LA, USA

    Yuping Wang

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  1. Chunlei Li
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  2. Chaodie Liu
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  3. Guangshuai Gao
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  4. Zhoufeng Liu
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  5. Yuping Wang
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Correspondence to Chunlei Li.

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Li, C., Liu, C., Gao, G. et al. Robust low-rank decomposition of multi-channel feature matrices for fabric defect detection. Multimed Tools Appl 78, 7321–7339 (2019). https://doi.org/10.1007/s11042-018-6483-6

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