A Rapid Crack Detection Technique Based on Attention for Intelligent M&amp;O of Cross-Sea Bridge

Yong-chuan ZHOU; Guang-jun LI; Wei WEI; Ya-meng WANG; Qiang JING

doi:10.1007/s13344-024-0068-0

Citation: Yong-chuan ZHOU, Guang-jun LI, Wei WEI, Ya-meng WANG and Qiang JING. A Rapid Crack Detection Technique Based on Attention for Intelligent M&O of Cross-Sea Bridge[J]. China Ocean Engineering, 2024, 38(5): 866-876. doi: 10.1007/s13344-024-0068-0 shu

A Rapid Crack Detection Technique Based on Attention for Intelligent M&O of Cross-Sea Bridge

1.
Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai 519015, China
2.
National Observation and Research Station of Material Corrosion and Structural Safety of Hong Kong-Zhuhai-Macao Bridge in Guangdong, Zhuhai 519000, China
3.
Wuhan Maritime Communication Research Institute, Wuhan 430079, China

Corresponding author: Wei WEI, 510320796@qq.com
Received Date: 2024-02-21
Accepted Date: 2024-05-06
Available Online: 2024-10-22

Figures(8) / Tables(5)

Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects. In practice, however, this task faces the challenge of finding a balance between detection accuracy and efficiency. To alleviate this problem, a lightweight and efficient real-time crack segmentation framework was developed. Specifically, in the network model system based on an encoding-decoding structure, the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers, and in the decoding process, we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features. Codecs are connected by pyramid pooling model (PPM) filtering. The results show that the crack segmentation accuracy and real-time operation capability larger than 76% and 15 fps, respectively, are validated by three publicly available datasets. These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.
- bridge defect detection,
- crack detection,
- lightweight design
1. [1]
  Badrinarayanan, V., Kendall, A. and Cipolla, R., 2017. SegNet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. doi: 10.1109/TPAMI.2016.2644615
2. [2]
  Ding, W., Yang, H., Yu, K. and Shu, J.P., 2023. Crack detection and quantification for concrete structures using UAV and transformer, Automation in Construction, 152, 104929. doi: 10.1016/j.autcon.2023.104929
3. [3]
  Dung, C.V. and Anh, L.D., 2019. Autonomous concrete crack detection using deep fully convolutional neural network, Automation in Construction, 99, 52–58. doi: 10.1016/j.autcon.2018.11.028
4. [4]
  Fu, H.X., Meng, D., Li, W.H., and Wang, Y.C., 2021. Bridge crack semantic segmentation based on improved Deeplabv3+, Journal of Marine Science and Engineering, 9(6), 671. doi: 10.3390/jmse9060671
5. [5]
  He, S.H., Zhao, X.M., Ma, J., Zhao, Y., Song, H.S., Song, H.X., Cheng, L., Yuan, Z.Y., Huang, F.W., Zhang, J., Tian, B., Wang, L.Y. and Qi, X.Z., 2017. Review of highway bridge inspection and condition assessment, China Journal of Highway and Transport, 30(11), 63−80. (in Chinese)
6. [6]
  Howard, A., Sandler, M., Chen, B., Wang, W.J., Chen, L.C., Tan, M.X., Chu, G., Vasudevan, V., Zhu, Y.K., Pang, R.M., Adam, H. and Le, Q., 2019. Searching for MobileNetV3, Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, Korea (South).
7. [7]
  Hsieh, Y.A. and Tsai, Y.J., 2020. Machine learning for crack detection: review and model performance comparison, Journal of Computing in Civil Engineering, 34(5), 04020038. doi: 10.1061/(ASCE)CP.1943-5487.0000918
8. [8]
  Jang, K., Jung, H. and An, Y.K., 2022. Automated bridge crack evaluation through deep super resolution network-based hybrid image matching, Automation in Construction, 137, 104229. doi: 10.1016/j.autcon.2022.104229
9. [9]
  Korthikanti, V.A., Casper, J., Lym, S., McAfee, L., Andersch, M., Shoeybi, M. and Catanzaro, B., 2023. Reducing activation recomputation in large transformer models, Proceedings of Machine Learning and Systems 5 Pre-Proceedings (MLSys 2023).
10. [10]
  Li, G., Liu, T., Fang, Z.Y., Shen, Q. and Ali, J., 2022a. Automatic bridge crack detection using boundary refinement based on real-time segmentation network, Structural Control & Health Monitoring, 29(9), e2991.
11. [11]
  Li, L.F., Wu, D. and Wang, N., 2021. Method for bridge crack detection based on multiresolution network, Laser & Optoelectronics Progress, 58(12), 1210004. (in Chinese)
12. [12]
  Li, Y., Li, Y.J., Liu J.C., Fan, Z. and Wang, Q.L., 2022b. Research on segmentation of steel surface defect images based on improved Res-UNet network, Journal of Electronics & Information Technology, 44(5), 1513−1520. (in Chinese)
13. [13]
  Liu, Z.Q., Cao, Y.W., Wang, Y.Z. and Wang, W., 2019. Computer vision-based concrete crack detection using U-net fully convolutional networks, Automation in Construction, 104, 129–139, doi: 10.1016/j.autcon.2019.04.005
14. [14]
  Long, L., Shelhamer, E., and Darrell, T., 2015. Fully convolutional networks for semantic segmentation, Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, MA, USA, pp. 3431–3440.
15. [15]
  Mehta, S., Rastegari, M., Shapiro, L. and Hajishirzi, H., 2019. Espnetv2: a light-weight, power efficient, and general purpose convolutional neural network, Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Long Beach, CA, USA, pp. 9190–9200.
16. [16]
  Milletari, F., Navab, N. and Ahmadi, S.A., 2016. V-Net: Fully convolutional neural networks for volumetric medical image segmentation, Proceedings of 2016 Fourth International Conference on 3D Vision (3DV), IEEE, Stanford, CA, USA .
17. [17]
  Peng, J.C., Liu, Y., Tang, S.Y., Hao, T.Y., Chu, L.Y., Chen, G.W., Wu, Z.W., Chen, Z.Y., Yu, Z.L., Du, Y.N., Dang, Q.Q., Lai, B.H., Liu, Q.W., Hu, X.G., Yu, D.H. and Ma, Y.J., 2022. PP-LiteSeg: A superior real-time semantic segmentation model, arXiv: 2204.02681v1.
18. [18]
  Poudel, R.P.K., Liwicki, S. and Cipolla, R, 2019. Fast-SCNN: Fast semantic segmentation network, arXiv: 1902.04502.
19. [19]
  Sandler, M., Howard, A., Zhu, M.L., Zhmoginov, A. and Chen, L.C., 2018. MobileNetV2: Inverted residuals and linear bottlenecks, Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, UT, USA.
20. [20]
  Song, Z.G., Liu, Y.L. and Zhang, C.X., 2023. Review on application and development of bridge crack detection based on machine vision, Science Technology and Engineering, 23(30), 12796–12805. (in Chinese)
21. [21]
  Sun, X.Z., Xie, Y.C., Jiang, L.M., Cao, Y. and Liu, B.Y., 2022. DMA-Net: DeepLab with multi-scale attention for pavement crack segmentation, IEEE Transactions on Intelligent Transportation Systems, 23(10), 18392–18403. doi: 10.1109/TITS.2022.3158670
22. [22]
  Wu, T.Y., Tang, S., Zhang, R., Cao, J. and Zhang, Y.D., 2021. CGNet: A light-weight context guided network for semantic segmentation, IEEE Transactions on Image Processing, 30, 1169–1179. doi: 10.1109/TIP.2020.3042065
23. [23]
  Yang, F., Zhang, L., Yu, S.J., Prokhorov, D., Mei, X. and Ling, H.B., 2020. Feature pyramid and hierarchical boosting network for pavement crack detection, IEEE Transactions on Intelligent Transportation Systems, 21(4), 1525–1535. doi: 10.1109/TITS.2019.2910595
24. [24]
  Yang, X.C., Li, H., Yu, Y.T., Luo, X.C., Huang, T. and Yang, X, 2018. Automatic pixel-level crack detection and measurement using fully convolutional network, Computer-Aided Civil and Infrastructure Engineering, 33(12), 1090–1109. doi: 10.1111/mice.12412
25. [25]
  Yu, C.Q., Gao, C.X., Wang, J.B., Yu, G., Shen, C.H. and Sang, N., 2021. BiSeNet V2: bilateral network with guided aggregation for real-time semantic segmentation, International Journal of Computer Vision, 129(11), 3051–3068. doi: 10.1007/s11263-021-01515-2
26. [26]
  Zhao, H.S., Shi, J.P., Qi, X.J., Wang, X.G. and Jia, J.Y., 2017. Pyramid scene parsing network, Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ), IEEE, Honolulu, HI, USA.
27. [27]
  Zhao, H.S., Qi, X.J., Shen, X.Y., Shi, J.P. and Jia, J.Y., 2018. ICNet for real-time semantic segmentation on high-resolution images, Proceedings of the 15th European Conference on Computer Vision–ECCV 2018, Springer, Munich, Germany.
28. [28]
  Zou, Q., Cao, Y., Li, Q.Q., Mao, Q.Z. and Wang, S, 2012. CrackTree: Automatic crack detection from pavement images, Pattern Recognition Letters, 33(3), 227–238. doi: 10.1016/j.patrec.2011.11.004
1. [1]
  . A Study on Crack Detection with Modal Parameters of A Jacket Platform. China Ocean Engineering, 2004, (1): -.
2. [2]
  . A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures. China Ocean Engineering, 2003, (3): -.
3. [3]
  李伟 , 陈国明 , 张传荣 , 刘涛 . Simulation Analysis and Experimental Study of Defect Detection Underwater by ACFM Probe. China Ocean Engineering, 2013, (2): 277-282.
4. [4]
  . Multilevel Optimal Design of Prestressed Lightweight Concrete-Steel Platform Structures. China Ocean Engineering, 2002, (1): -.
5. [5]
  . Leak Detection in Offshore Pipelines of Conveying Fluid. China Ocean Engineering, 2004, (2): -.
6. [6]
  . Estimation of Probability of Damage Detection in Offshore Structural Inspection. China Ocean Engineering, 1996, (3): -.
7. [7]
  . The CMEKF Method for Sub-Sea Pipeline Monitoring and Leak Detection. China Ocean Engineering, 2004, (4): -.
8. [8]
  . Research on Maneuverability and Simulation of Underwater Vehicles for Pipeline Detection and Maintenance. China Ocean Engineering, 2006, (1): -.
9. [9]
  王文光 , 孙作为 , 李晨鸣 , 王俊 . Study on Dim Target Detection and Discrimination from Sea Clutter. China Ocean Engineering, 2013, (2): 183-192.
10. [10]
  张铁栋 , 万磊 , 曾文静 , 徐玉如 . Object Detection and Tracking Method of AUV Based on Acoustic Vision. China Ocean Engineering, 2012, (4): 623-636.
11. [11]
  . Damage Detection Methods for Offshore Platforms Based on Wavelet Packet Transform. China Ocean Engineering, 2005, (4): -.
12. [12]
  . Experiment Verification of Damage Detection for Offshore Platforms by Neural Networks. China Ocean Engineering, 2006, (3): -.
13. [13]
  Y. C. Lin , C. C. Chang , M. C. Lee , H. C. Chan , J. M. Leu . Detection of Breaking Waves Using X-Band Pulse Radar in the Nearshore Region. China Ocean Engineering, 2013, (4): 549-556.
14. [14]
  Mojtahedi, A. , Lotfollahi Yaghin, M. A. , Hassanzadeh, Y. , Abbasidoust, F. , Ettefagh, M. M. , Aminfar, M. H. . A Robust Damage Detection Method Developed for Offshore Jacket Platforms Using Modified Artificial Immune System Algorithm. China Ocean Engineering, 2012, (3): 379-395.
15. [15]
  Qiming Wang , Ruihu Zhu , Jinhai Zheng , Ning Wang , Mengyan luo , Yufei Che . A method for the damage detection of pile foundation in high-pile wharf based on a curvature mode deletion model. China Ocean Engineering, 2020, (): -.
16. [16]
  Qi-ming WANG , Rui-hu ZHU , Jin-hai ZHENG , Ning WANG , Meng-yan LUO , Yu-fei CHE . A Method for the Damage Detection of Pile Foundation in High-Pile Wharf Based on A Curvature Mode Deletion Model. China Ocean Engineering, 2020, 34(6): 871-880. doi: 10.1007/s13344-020-0079-4
17. [17]
  Jie ZHAO , Xiong-bo ZHENG , Tong-yao MU , Yue-yue LENG , Hao-yu ZHAO . Study of the Boundary Pressure Instability of the SPH Method Based on Fan-Free Surface Detection. China Ocean Engineering, 2025, 39(2): 340-353. doi: 10.1007/s13344-025-0026-5
18. [18]
  . Strength of Lightweight Concrete Under Triaxial Compression. China Ocean Engineering, 1996, (2): -.
19. [19]
  . Temperature and Stress Analysis of An Experimental Model of Prestressed Lightweight Concrete Platform. China Ocean Engineering, 2000, (3): -.
20. [20]
  . A Probabilistic Model for Fatigue Crack Propagation Analysis. China Ocean Engineering, 1999, (4): -.

Get Citation

PDF

XML

Metrics

PDF Downloads(17)
Abstract views(3384)
HTML views(2523)
Cited By(0)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142