IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/8826593.html
   My bibliography  Save this article

Detecting Small Chinese Traffic Signs via Improved YOLOv3 Method

Author

Listed:
  • Baojun Zhang
  • Guili Wang
  • Huilan Wang
  • Chenchen Xu
  • Yu Li
  • Lin Xu

Abstract

Long-distance detection of traffic signs provides drivers with more reaction time, which is an effective technique to reduce the probability of sudden accidents. It is recognized that the imaging size of far traffic signs is decreasing with distance. Such a fact imposes much challenge on long-distance detection. Aiming to enhance the recognition rate of long-distance small targets, we design a four-scale detection structure based on the three-scale detection structure of YOLOv3 network. In order to reduce the occlusion effects of similar objects, NMS is replaced by soft-NMS. In addition, the datasets are trained and the K-Means method is used to generate the appropriate anchor boxes, so as to speed up the network computing. By using these methods, better experimental results for the recognition of long-distance traffic signs have been obtained. The recognition rate is 43.8 frames per second (FPS), and the recognition accuracy is improved to 98.8%, which is much better than the original YOLOv3.

Suggested Citation

  • Baojun Zhang & Guili Wang & Huilan Wang & Chenchen Xu & Yu Li & Lin Xu, 2021. "Detecting Small Chinese Traffic Signs via Improved YOLOv3 Method," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, February.
    • Handle: RePEc:hin:jnlmpe:8826593
    DOI: 10.1155/2021/8826593
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/8826593.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/8826593.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/8826593?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
    ---><---

    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:8826593. 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.