IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i10p2377-d1151461.html
   My bibliography  Save this article

An Improved YOLOv5 Crack Detection Method Combined with a Bottleneck Transformer

Author

Listed:
  • Gui Yu

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    School of Mechatronic and Intelligent Manufacturing, Huanggang Normal University, Huanggang 438000, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
    School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Xinglin Zhou

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
    School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

Efficient detection of pavement cracks can effectively prevent traffic accidents and reduce road maintenance costs. In this paper, an improved YOLOv5 network combined with a Bottleneck Transformer is proposed for crack detection, called YOLOv5-CBoT. By combining the CNN and Transformer, YOLOv5-CBoT can better capture long-range dependencies to obtain more global information, so as to adapt to the long-span detection task of cracks. Moreover, the C2f module, which is proposed in the state-of-the-art object detection network YOLOv8, is introduced to further optimize the network by paralleling more gradient flow branches to obtain richer gradient information. The experimental results show that the improved YOLOv5 network has achieved competitive results on RDD2020 dataset, with fewer parameters and lower computational complexity but with higher accuracy and faster inference speed.

Suggested Citation

  • Gui Yu & Xinglin Zhou, 2023. "An Improved YOLOv5 Crack Detection Method Combined with a Bottleneck Transformer," Mathematics, MDPI, vol. 11(10), pages 1-12, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2377-:d:1151461
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/10/2377/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/10/2377/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Jae Hyun Yoon & Jong Won Jung & Seok Bong Yoo, 2024. "Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
    2. Bo Yu & Qi Li & Wenhua Jiao & Shiyang Zhang & Yongjun Zhu, 2024. "SAB-YOLOv5: An Improved YOLOv5 Model for Permanent Magnetic Ferrite Magnet Rotor Detection," Mathematics, MDPI, vol. 12(7), pages 1-17, March.
    3. Feng Xiao & Haibin Wang & Yueqin Xu & Zhen Shi, 2023. "A Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm," Agriculture, MDPI, vol. 14(1), pages 1-18, December.

    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:jmathe:v:11:y:2023:i:10:p:2377-:d:1151461. 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.