IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5395494.html
   My bibliography  Save this article

Multitype Damage Detection of Container Using CNN Based on Transfer Learning

Author

Listed:
  • Zixin Wang
  • Jing Gao
  • Qingcheng Zeng
  • Yuhui Sun

Abstract

Due to the repeated bearing of mechanical operations and natural factors, the container will suffer various types of damage during use. Adopting effective container damage detection methods plays a vital role in prolonging the service life and using function. This paper proposes a multitype damage detection model for containers based on transfer learning and MobileNetV2. In addition, a data set containing nine typical types of container damage is established. To ensure the validity and practicability of the model, we conducted tests and verifications in the actual port environment. The results show that the model can identify multiple types of container damage. Compared with the existing models, the damage detection model proposed in this paper can ensure the identification effect of various types of container damage, which is more suitable for the actual container detection situation. This method can provide a new idea of damage detection for container management in ports.

Suggested Citation

  • Zixin Wang & Jing Gao & Qingcheng Zeng & Yuhui Sun, 2021. "Multitype Damage Detection of Container Using CNN Based on Transfer Learning," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, August.
    • Handle: RePEc:hin:jnlmpe:5395494
    DOI: 10.1155/2021/5395494
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/5395494.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/5395494.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/5395494?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Delgado, Guillem & Cortés, Andoni & García, Sara & Loyo, Estíbaliz & Berasategi, Maialen & Aranjuelo, Nerea, 2023. "Methodology for generating synthetic labeled datasets for visual container inspection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:5395494. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.