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Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework

Author

Listed:
  • Feiyang Li

    (Guangdong University of Technology)

  • Nian Cai

    (Guangdong University of Technology
    Huizhou Guangdong University of Technology IoT Cooperative Innovation Institute Co., Ltd.)

  • Xueliang Deng

    (Guangdong University of Technology)

  • Jiahao Li

    (Guangdong University of Technology)

  • Jianfa Lin

    (Foshan Deeple Vision Technology Co. Ltd.)

  • Han Wang

    (Guangdong University of Technology)

Abstract

The ceramic membrane plays an important role in the wastewater disposal industry. The serial number engraved on each ceramic membrane is an essential feature for identification. Here, an automatic inspection system for serial numbers of ceramic membranes is proposed to replace the manual inspection. To the best of our knowledge, this is the first attempt to automatically inspect serial numbers of ceramic membranes. To suppress error accumulation inherently existed in the previous stepwise approaches, an end-to-end photometric-induced convolutional neural network framework is proposed for this automatic inspection system. The framework consists of three sequential stages, which are photometric stage for performing photometric stereo, localization stage for localizing the text region, and recognition stage for producing recognition results. The photometric stage can integrate three-dimensional shape information of serial numbers of ceramic membranes into the framework to improve the inspection performance. Since three stages are jointly trained, a theoretical analysis on the contributions of the local losses is provided to ensure the convergence of the framework, which can guide the design of the total loss function of the framework. Experimental results demonstrate that the proposed framework achieves better inspection performance with a reasonable inspection time compared with the state-of-the-art deep learning methods, whose localization performance and recognition performance are the F-score of 95.61% and the accuracy of 96.49%, respectively. Furthermore, these demonstrate the potential that our proposed automatic inspection system will be beneficial for the intelligentialize of the ceramic membrane manufacturing and wastewater treatment if it is equipped with a perception system and a control system in ceramic membrane production lines and wastewater treatment processes.

Suggested Citation

  • Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-020-01730-7
    DOI: 10.1007/s10845-020-01730-7
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    References listed on IDEAS

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    1. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    2. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    3. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
    4. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
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