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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
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    Citations

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    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.
    4. Nizar Faisal Alkayem & Ali Mayya & Lei Shen & Xin Zhang & Panagiotis G. Asteris & Qiang Wang & Maosen Cao, 2024. "Co-CrackSegment: A New Collaborative Deep Learning Framework for Pixel-Level Semantic Segmentation of Concrete Cracks," Mathematics, MDPI, vol. 12(19), pages 1-37, October.

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