Crack Detection of Concrete Bridges Based on Improved U-Net Model

Zhen Huang; Xingtuo Zhang; Kaizhong Xie; Xiaojun Ke; Yiyi Zhang

doi:doi:10.11648/j.sd.20221006.29

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Crack Detection of Concrete Bridges Based on Improved U-Net Model

Zhen Huang, Xingtuo Zhang, Kaizhong Xie, Xiaojun Ke, Yiyi Zhang

Published in Science Discovery (Volume 10, Issue 6)

Received: 2 November 2022 Accepted: 29 November 2022 Published: 8 December 2022

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Abstract

Bridge is an important part of traffic infrastructure, and its maintenance is related to smooth traffic and even the safety of people's lives and property. Automatic detection of concrete bridge cracks is an important part of bridge maintenance. However, it is still a challenging task due to the inhomogeneous strength of concrete bridge cracks, the complexity of the background, and the weakness of the target. Currently, traditional image processing-based technology cannot detect bridge cracks well, and the typical deep learning model is not very effective when directly used to detect bridge cracks. In response to this problem, this paper proposes a bridge crack detection model based on the improved U-Net deep learning model. Compared with the original U-Net model, the convolution block of our improved model is wider in the encoding stage, and the wider convolution block enables the model to learn more semantically discriminative information. Finally, the performance of the improved model is validated on a dataset with 600 images. Experimental results show that compared with other advanced detection models, the proposed method achieves better performance in terms of average intersection ratio and pixel accuracy. Therefore, the method proposed in this paper can detect concrete bridge cracks automatically, which provides a certain reference value for bridge crack maintenance engineers based on image recognition technology.

Published in	Science Discovery (Volume 10, Issue 6)
DOI	10.11648/j.sd.20221006.29
Page(s)	500-505
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Deep Learning, Bridge Crack Detection, U-Net, Convolutional Neural Networks

References

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APA Style

Zhen Huang, Xingtuo Zhang, Kaizhong Xie, Xiaojun Ke, Yiyi Zhang. (2022). Crack Detection of Concrete Bridges Based on Improved U-Net Model. Science Discovery, 10(6), 500-505. https://doi.org/10.11648/j.sd.20221006.29

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ACS Style

Zhen Huang; Xingtuo Zhang; Kaizhong Xie; Xiaojun Ke; Yiyi Zhang. Crack Detection of Concrete Bridges Based on Improved U-Net Model. Sci. Discov. 2022, 10(6), 500-505. doi: 10.11648/j.sd.20221006.29

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AMA Style

Zhen Huang, Xingtuo Zhang, Kaizhong Xie, Xiaojun Ke, Yiyi Zhang. Crack Detection of Concrete Bridges Based on Improved U-Net Model. Sci Discov. 2022;10(6):500-505. doi: 10.11648/j.sd.20221006.29

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@article{10.11648/j.sd.20221006.29,
  author = {Zhen Huang and Xingtuo Zhang and Kaizhong Xie and Xiaojun Ke and Yiyi Zhang},
  title = {Crack Detection of Concrete Bridges Based on Improved U-Net Model},
  journal = {Science Discovery},
  volume = {10},
  number = {6},
  pages = {500-505},
  doi = {10.11648/j.sd.20221006.29},
  url = {https://doi.org/10.11648/j.sd.20221006.29},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20221006.29},
  abstract = {Bridge is an important part of traffic infrastructure, and its maintenance is related to smooth traffic and even the safety of people's lives and property. Automatic detection of concrete bridge cracks is an important part of bridge maintenance. However, it is still a challenging task due to the inhomogeneous strength of concrete bridge cracks, the complexity of the background, and the weakness of the target. Currently, traditional image processing-based technology cannot detect bridge cracks well, and the typical deep learning model is not very effective when directly used to detect bridge cracks. In response to this problem, this paper proposes a bridge crack detection model based on the improved U-Net deep learning model. Compared with the original U-Net model, the convolution block of our improved model is wider in the encoding stage, and the wider convolution block enables the model to learn more semantically discriminative information. Finally, the performance of the improved model is validated on a dataset with 600 images. Experimental results show that compared with other advanced detection models, the proposed method achieves better performance in terms of average intersection ratio and pixel accuracy. Therefore, the method proposed in this paper can detect concrete bridge cracks automatically, which provides a certain reference value for bridge crack maintenance engineers based on image recognition technology.},
 year = {2022}
}

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TY  - JOUR
T1  - Crack Detection of Concrete Bridges Based on Improved U-Net Model
AU  - Zhen Huang
AU  - Xingtuo Zhang
AU  - Kaizhong Xie
AU  - Xiaojun Ke
AU  - Yiyi Zhang
Y1  - 2022/12/08
PY  - 2022
N1  - https://doi.org/10.11648/j.sd.20221006.29
DO  - 10.11648/j.sd.20221006.29
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 500
EP  - 505
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20221006.29
AB  - Bridge is an important part of traffic infrastructure, and its maintenance is related to smooth traffic and even the safety of people's lives and property. Automatic detection of concrete bridge cracks is an important part of bridge maintenance. However, it is still a challenging task due to the inhomogeneous strength of concrete bridge cracks, the complexity of the background, and the weakness of the target. Currently, traditional image processing-based technology cannot detect bridge cracks well, and the typical deep learning model is not very effective when directly used to detect bridge cracks. In response to this problem, this paper proposes a bridge crack detection model based on the improved U-Net deep learning model. Compared with the original U-Net model, the convolution block of our improved model is wider in the encoding stage, and the wider convolution block enables the model to learn more semantically discriminative information. Finally, the performance of the improved model is validated on a dataset with 600 images. Experimental results show that compared with other advanced detection models, the proposed method achieves better performance in terms of average intersection ratio and pixel accuracy. Therefore, the method proposed in this paper can detect concrete bridge cracks automatically, which provides a certain reference value for bridge crack maintenance engineers based on image recognition technology.
VL  - 10
IS  - 6
ER  -

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Author Information

Zhen Huang

Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning, China
Xingtuo Zhang

Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning, China
Kaizhong Xie

Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning, China
Xiaojun Ke

Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning, China
Yiyi Zhang

Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning, China

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Plain Text BibTeX RIS

APA Style

Zhen Huang, Xingtuo Zhang, Kaizhong Xie, Xiaojun Ke, Yiyi Zhang. (2022). Crack Detection of Concrete Bridges Based on Improved U-Net Model. Science Discovery, 10(6), 500-505. https://doi.org/10.11648/j.sd.20221006.29

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ACS Style

Zhen Huang; Xingtuo Zhang; Kaizhong Xie; Xiaojun Ke; Yiyi Zhang. Crack Detection of Concrete Bridges Based on Improved U-Net Model. Sci. Discov. 2022, 10(6), 500-505. doi: 10.11648/j.sd.20221006.29

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AMA Style

Zhen Huang, Xingtuo Zhang, Kaizhong Xie, Xiaojun Ke, Yiyi Zhang. Crack Detection of Concrete Bridges Based on Improved U-Net Model. Sci Discov. 2022;10(6):500-505. doi: 10.11648/j.sd.20221006.29

Copy | Download

@article{10.11648/j.sd.20221006.29,
  author = {Zhen Huang and Xingtuo Zhang and Kaizhong Xie and Xiaojun Ke and Yiyi Zhang},
  title = {Crack Detection of Concrete Bridges Based on Improved U-Net Model},
  journal = {Science Discovery},
  volume = {10},
  number = {6},
  pages = {500-505},
  doi = {10.11648/j.sd.20221006.29},
  url = {https://doi.org/10.11648/j.sd.20221006.29},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20221006.29},
  abstract = {Bridge is an important part of traffic infrastructure, and its maintenance is related to smooth traffic and even the safety of people's lives and property. Automatic detection of concrete bridge cracks is an important part of bridge maintenance. However, it is still a challenging task due to the inhomogeneous strength of concrete bridge cracks, the complexity of the background, and the weakness of the target. Currently, traditional image processing-based technology cannot detect bridge cracks well, and the typical deep learning model is not very effective when directly used to detect bridge cracks. In response to this problem, this paper proposes a bridge crack detection model based on the improved U-Net deep learning model. Compared with the original U-Net model, the convolution block of our improved model is wider in the encoding stage, and the wider convolution block enables the model to learn more semantically discriminative information. Finally, the performance of the improved model is validated on a dataset with 600 images. Experimental results show that compared with other advanced detection models, the proposed method achieves better performance in terms of average intersection ratio and pixel accuracy. Therefore, the method proposed in this paper can detect concrete bridge cracks automatically, which provides a certain reference value for bridge crack maintenance engineers based on image recognition technology.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Crack Detection of Concrete Bridges Based on Improved U-Net Model
AU  - Zhen Huang
AU  - Xingtuo Zhang
AU  - Kaizhong Xie
AU  - Xiaojun Ke
AU  - Yiyi Zhang
Y1  - 2022/12/08
PY  - 2022
N1  - https://doi.org/10.11648/j.sd.20221006.29
DO  - 10.11648/j.sd.20221006.29
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 500
EP  - 505
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20221006.29
AB  - Bridge is an important part of traffic infrastructure, and its maintenance is related to smooth traffic and even the safety of people's lives and property. Automatic detection of concrete bridge cracks is an important part of bridge maintenance. However, it is still a challenging task due to the inhomogeneous strength of concrete bridge cracks, the complexity of the background, and the weakness of the target. Currently, traditional image processing-based technology cannot detect bridge cracks well, and the typical deep learning model is not very effective when directly used to detect bridge cracks. In response to this problem, this paper proposes a bridge crack detection model based on the improved U-Net deep learning model. Compared with the original U-Net model, the convolution block of our improved model is wider in the encoding stage, and the wider convolution block enables the model to learn more semantically discriminative information. Finally, the performance of the improved model is validated on a dataset with 600 images. Experimental results show that compared with other advanced detection models, the proposed method achieves better performance in terms of average intersection ratio and pixel accuracy. Therefore, the method proposed in this paper can detect concrete bridge cracks automatically, which provides a certain reference value for bridge crack maintenance engineers based on image recognition technology.
VL  - 10
IS  - 6
ER  -

Copy | Download