IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i2d10.1007_s10845-021-01830-y.html
   My bibliography  Save this article

Automatic recognition of hot spray marking dot-matrix characters for steel-slab industry

Author

Listed:
  • Junhui Ge

    (Hunan University)

  • Licheng Liu

    (Hunan University)

  • Junxi Sun

    (NorthEast Normal University)

  • Hong Zhao

    (National University of Defense Technology)

  • Langming Zhou

    (Hunan University)

  • Tianle Cheng

    (Hunan University)

  • Changyan Xiao

    (Hunan University)

Abstract

The automatic recognition of labels marked on steel slab surfaces is of significance to information management and intelligent manufacturing in steel plants. However, it is not an easy task due to complex factors like low printing quality, motion distortion and thermal blurring, especially while handling the prone-to-deform dot-matrix labels generated by a hot spray marking (HSM) technique. In this paper, a machine vision system is presented for the HSM dot-matrix label recognition. With a brief description of the imaging system, our emphasis is put on image analysis. First, a coarse-to-fine strategy is applied to locate HSM characters from captured images, where a weighted gravity-center estimation method is extended to search the enclosure of label regions, and an edge projection scheme is adopted to refine the label extraction. Subsequently, a Multidirectional Line Scanning (MLS) method is proposed to determine the boundaries between adjacent dot-matrix characters with tilt, adhesion or dot-missing abnormalities. Finally, by converting the dot-matrix character into a 2D point set, we introduce a Point Cloud registration for DOt-matrix Character (PC4DOC) method to recognize prone-to-deform characters, which appears to accommodate various distortions and abnormalities owing to the inherent deformation correction of affine transformation and fault tolerance of robust correspondence matching. According to our experiments, the proposed method can achieve real-time recognition with an accuracy of 93.84% in spite of severely degraded images and incomplete characters. The system has been installed and run in a steel mill for more than one year, and its stability was also verified.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01830-y
    DOI: 10.1007/s10845-021-01830-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01830-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01830-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Keyur D. Joshi & Vedang Chauhan & Brian Surgenor, 2020. "A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 103-125, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01830-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.