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Automated Tire visual inspection based on low rank matrix recovery

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Abstract

Visual inspection is a challenging and widely employed process in industries. In this work, an automated tire visual inspection system is proposed based on low rank matrix recovery. Deep Network is employed to perform texture segmentation which benefits low rank decomposition in both quality and computational efficiency. We propose a dual optimization method to improve convergence speed and matrix sparsity by incorporating the improvement of the soft-threshold shrinkage operator by the weight matrix M. We investigated how incremental multiplier affects the decomposition accuracy and the convergence speed of the algorithm. On this basis, image blocks were decomposed into low-rank matrix and sparse matrix in which defects were separated. Comparative experiments have been performed on our dataset. Experimental results validate the theoretical analysis. The method is promising in false alarm, robustness and running time based on multi-core processor distributed computing. It can be extended to other real-time industrial applications.

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Acknowledgements

This work was supported by the Natural Science Foundation of Shandong Province No. ZR2019MEE066 and partly by No. ZR2018MC007.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China

    Guangxu Li, Yuyi Shao, Jinyue Shen & Yan Zhang

  2. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China

    Zhouzhou Zheng

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  1. Guangxu Li
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  2. Zhouzhou Zheng
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  3. Yuyi Shao
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  4. Jinyue Shen
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  5. Yan Zhang
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Correspondence to Yan Zhang.

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Li, G., Zheng, Z., Shao, Y. et al. Automated Tire visual inspection based on low rank matrix recovery. Multimed Tools Appl 82, 24227–24246 (2023). https://doi.org/10.1007/s11042-023-14467-1

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  • DOI: https://doi.org/10.1007/s11042-023-14467-1

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