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Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry

Author

Listed:
  • Pedro Malaca

    (University of Porto
    SARKKIS Robotics, Lda.)

  • Luis F. Rocha

    (INESC-TEC, INESC Technology and Science)

  • D. Gomes

    (University of Coimbra)

  • João Silva

    (SARKKIS Robotics, Lda.)

  • Germano Veiga

    (INESC-TEC, INESC Technology and Science)

Abstract

This paper focus on the classification, in real-time and under uncontrolled lighting, of fabric textures for the automotive industry. Many industrial processes have spatial constraints that limit the effective control of illumination of their vision based systems, hindering their effectiveness. The ability to overcome these problems using robust classification methods with suitable pre-processing techniques and choice of characteristics will increase the efficiency of this type of solutions with obvious production gains and thus economical. For this purpose, this paper studied and analyzed various pre-processing techniques, and selected the most appropriate fabric characteristics for the considered industrial case scenario. The methodology followed was based on the comparison of two different machine learning classifiers, ANN and SVM, using a large set of samples with a large variability of lightning conditions to faithfully simulate the industrial environment. The obtained solution shows the sensibility of ANN over SVM considering the number of features and the size of the training set, showing the better effectiveness and robustness of the last. The characteristics vector uses histogram equalization, Laws filter and Sobel filter, and multi-scale analysis. By using a correlation based method was possible to reduce the number of features used, achieving a better balanced between processing time and classification ratio.

Suggested Citation

  • Pedro Malaca & Luis F. Rocha & D. Gomes & João Silva & Germano Veiga, 2019. "Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 351-361, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1254-6
    DOI: 10.1007/s10845-016-1254-6
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    References listed on IDEAS

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    1. Nicolas Pinto & David D Cox & James J DiCarlo, 2008. "Why is Real-World Visual Object Recognition Hard?," PLOS Computational Biology, Public Library of Science, vol. 4(1), pages 1-6, January.
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    Cited by:

    1. Junhui Ge & Licheng Liu & Junxi Sun & Hong Zhao & Langming Zhou & Tianle Cheng & Changyan Xiao, 2023. "Automatic recognition of hot spray marking dot-matrix characters for steel-slab industry," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 869-884, February.
    2. Mohamed Ismail & Noha A. Mostafa & Ahmed El-assal, 2022. "Quality monitoring in multistage manufacturing systems by using machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2471-2486, December.
    3. Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
    4. Jie Zhang & Pengpeng Yao & Hochung Wu & John H. Xin, 2023. "Automatic color pattern recognition of multispectral printed fabric images," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2747-2763, August.
    5. Carlos A. Escobar & Megan E. McGovern & Ruben Morales-Menendez, 2021. "Quality 4.0: a review of big data challenges in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2319-2334, December.
    6. Huixin Tian & Daixu Ren & Kun Li & Zhen Zhao, 2021. "An adaptive update model based on improved Long Short Term Memory for online prediction of vibration signal," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 37-49, January.

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