IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12274-d926820.html
   My bibliography  Save this article

Real-Time Vehicle Detection Based on Improved YOLO v5

Author

Listed:
  • Yu Zhang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Zhongyin Guo

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Jianqing Wu

    (School of Qilu Transportation, Shandong University, Jinan 250061, China
    Suzhou Research Institute, Shandong University, Suzhou 215000, China)

  • Yuan Tian

    (School of Qilu Transportation, Shandong University, Jinan 250061, China
    Suzhou Research Institute, Shandong University, Suzhou 215000, China)

  • Haotian Tang

    (School of Qilu Transportation, Shandong University, Jinan 250061, China)

  • Xinming Guo

    (School of Qilu Transportation, Shandong University, Jinan 250061, China
    Suzhou Research Institute, Shandong University, Suzhou 215000, China)

Abstract

To reduce the false detection rate of vehicle targets caused by occlusion, an improved method of vehicle detection in different traffic scenarios based on an improved YOLO v5 network is proposed. The proposed method uses the Flip-Mosaic algorithm to enhance the network’s perception of small targets. A multi-type vehicle target dataset collected in different scenarios was set up. The detection model was trained based on the dataset. The experimental results showed that the Flip-Mosaic data enhancement algorithm can improve the accuracy of vehicle detection and reduce the false detection rate.

Suggested Citation

  • Yu Zhang & Zhongyin Guo & Jianqing Wu & Yuan Tian & Haotian Tang & Xinming Guo, 2022. "Real-Time Vehicle Detection Based on Improved YOLO v5," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12274-:d:926820
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12274/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12274/
    Download Restriction: no
    ---><---

    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:gam:jsusta:v:14:y:2022:i:19:p:12274-:d:926820. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.