Volume 24 Issue 6
Dec.  2024
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(6): 135-147
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JIANG Shi-xin, ZOU Xiao-xue, YANG Jian-xi, LI Hao, HUANG Xue-mei, LI Ren, ZHANG Ting-ping, LIU Xin-long, WANG Di. Concrete bridge crack detection method based on improved YOLO v8s in complex backgrounds[J]. Journal of Traffic and Transportation Engineering, 2024, 24(6): 135-147. doi: 10.19818/j.cnki.1671-1637.2024.06.009
Citation: JIANG Shi-xin, ZOU Xiao-xue, YANG Jian-xi, LI Hao, HUANG Xue-mei, LI Ren, ZHANG Ting-ping, LIU Xin-long, WANG Di. Concrete bridge crack detection method based on improved YOLO v8s in complex backgrounds[J]. Journal of Traffic and Transportation Engineering, 2024, 24(6): 135-147. doi: 10.19818/j.cnki.1671-1637.2024.06.009
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Concrete bridge crack detection method based on improved YOLO v8s in complex backgrounds

doi: 10.19818/j.cnki.1671-1637.2024.06.009
  • 1.

    School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

  • 2.

    College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Natural Science Foundation of China 62003063

National Natural Science Foundation of China 62103068

Natural Science Foundation of Chongqing CSTB2022NSCQ-MSX1599

Natural Science Foundation of Chongqing cstc2020jcyj-msxmX0047

Science and Technology Research Project of Chongqing Municipal Education Commission KJZD-K202400709

Science and Technology Research Project of Chongqing Municipal Education Commission KJZD-M202300703

Science and Technology Research Project of Chongqing Municipal Education Commission KJQN202100748

More Information
  • Author Bio:

    JIANG Shi-xin(1992-), male, associate professor, PhD, shixinjiang@cqjtu.edu.cn

  • Received Date: 2024-05-11
  • Publish Date: 2024-12-25
    Abstract
  • To address the issue of low detection accuracy for cracks in concrete bridges caused by complex backgrounds, as well as small and obscure features, the crack location information was accurately located, and key parameters such as crack length and width were measured based on the improved YOLO v8s algorithm for concrete bridge crack detection. Based on the YOLO v8s model, the omni-dimensional dynamic convolution (ODConv) was incorporated to capture richer contextual information in feature maps, enhancing the model's ability to extract the target features and detect small and obscure cracks. An improved channel attention module was used to develop the concatenated two-layer feature-modified attention (C2f-MA) fusion module, enabling the extraction of more texture information from feature maps. This modification further made the network focus on crack features, suppressing interferences from irrelevant background information and improving the crack detection performance in complex backgrounds. The weighted intersection over union (WIoU) loss function was introduced to address the challenge of low-quality sample recognition, optimizing the model's convergence speed and detection accuracy. Crack images with complex backgrounds such as small and obscure cracks, shadows, artificial lines, and weeds were screened in the bridge detection report. A bridge crack image dataset was established by manual annotation with complex background conditions. The model's performance was comprehensively evaluated through comparative and ablation experiments by taking recall, average precision and model storage capacity as quantitative evaluation indicators. Research results demonstrate that the improved YOLO v8s algorithm achieves recall, average precision of 0.829, 0.893 and 0.631, respectively, as well as the model storage capacity of 11.14 MB. Its comprehensive evaluation indicators outperforms the baseline YOLO v8s and other target detection models, validating that the proposed algorithm exhibits robust performance in complex backgrounds.

     

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