Structural Monitoring and Maintenance

  • 제9권3호
  • /
  • Pages.289-303
  • /
  • 2022
  • /
  • 2288-6605(pISSN)
  • /
  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Pixel-based crack image segmentation in steel structures using atrous separable convolution neural network

  • Ta, Quoc-Bao (Department of Ocean Engineering, Pukyong National University) ;
  • Pham, Quang-Quang (Department of Ocean Engineering, Pukyong National University) ;
  • Kim, Yoon-Chul (Department of Civil Engineering, Yonsei University) ;
  • Kam, Hyeon-Dong (Department of Ocean Engineering, Pukyong National University) ;
  • Kim, Jeong-Tae (Department of Ocean Engineering, Pukyong National University)
  • 투고 : 2022.07.09
  • 심사 : 2022.09.29
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.

키워드

과제정보

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (2022R1A2C10038891161782064340101). The datasets used in this paper were granted by the committee of the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020). The authors would like to thank for the opportunity provided by IPC-SHM 2020.

참고문헌

  1. Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaria, J., Fadhel, M.A., Al-Amidie, M. and Farhan, L. (2021), "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions", J. Big Data, 8 (1), 1-74. https://doi.org/10.1186/s40537-021-00444-8
  2. Bailly, A., Blanc, C., Francis, E., Guillotin, T., Jamal, F., Wakim, B. and Roy, P. (2022), "Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models", Comput. Methods Programs Biomed., 213, 106504. https://doi.org/10.1016/j.cmpb.2021.106504
  3. Bao, Y., Li, J., Nagayama, T., Xu, Y., Spencer, B.F. and Li, H. (2021), "The 1st international project competition for structural health monitoring (IPC-SHM, 2020): A summary and benchmark problem", Struct. Health Monitor., 20(4), 2229-2239. https://doi.org/10.1177/14759217211006485
  4. Barbedo, J.G.A. (2018), "Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification", Comput. Electron Agric., 153, 46-53. https://doi.org/10.1016/j.compag.2018.08.013
  5. Battista, R. and Pfeil, M. (1999), "Fatigue cracks induced by traffic loading on steel bridges' slender orthotropic decks", The Ninth International Conference on Computational Methods and Experimental Measurements", WIT Transact. Modell. Simul., 21, 37-46. https://www.witpress.com/elibrary/wit-transactions-on-modelling-and-simulation/22/4967
  6. Campbell, L.E., Connor, R.J., Whitehead, J.M. and Washer, G.A. (2021), "Human factors affecting visual inspection of fatigue cracking in steel bridges", Struct. Infrastruct. Eng., 17(11), 1447-1458. https://doi.org/10.1080/15732479.2020.1813783
  7. Cha, Y.-J., Choi, W. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput.-Aided Civ. Infrastruct. Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263
  8. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K. and Yuille, A.L. (2018), "Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs", IEEE Trans. Pattern. Anal. Mach. Intell., 40(4), 834-848. Doi: 10.1109/TPAMI.2017.2699184
  9. Dong, C., Li, L., Yan, J., Zhang, Z., Pan, H. and Catbas, F.N. (2021), "Pixel-level fatigue crack segmentation in large-scale images of steel structures using an encoder-decoder network", Sensors, 21(12). https://doi.org/10.3390/s21124135
  10. Dung, C.V. and Anh, L.D. (2019), "Autonomous concrete crack detection using deep fully convolutional neural network", Autom. Constr., 99, 52-58. https://doi.org/10.1016/j.autcon.2018.11.028
  11. Dung, C.V., Sekiya, H., Hirano, S., Okatani, T. and Miki, C. (2019), "A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks", Autom. Constr., 102, 217-229. https://doi.org/10.1016/j.autcon.2019.02.013
  12. Gallwey, T.J. (1998a), "Evaluation and control of industrial inspection: Part I-Guidelines for the practitioner", Int. J. Ind. Ergon., 22(1-2), 37-49. https://doi.org/10.1016/S0169-8141(97)00066-8
  13. Gallwey, T.J. (1998b), "Evaluation and control of industrial inspection: Part II-The scientific basis for the guide", Int. J. Ind. Ergon., 22(1-2), 51-65. https://doi.org/10.1016/S0169-8141(97)00067-X
  14. Lee, J.K., Bae, D.S., Lee, S.P. and Lee, J.H. (2014), "Evaluation on defect in the weld of stainless steel materials using nondestructive technique", Fusion Eng. Des., 89(7-8), 1739-1745. https://doi.org/10.1016/j.fusengdes.2013.12.026
  15. Mutlib, N.K., Baharom, S.B., El-Shafie, A. and Nuawi, M.Z. (2016), "Ultrasonic health monitoring in structural engineering: buildings and bridges", Struct. Control Health Monit., 23(3), 409-422. https://doi.org/10.1002/stc.1800
  16. Ronneberger, O., Fischer, P. and Brox, T. (2015), "U-net: Convolutional networks for biomedical image segmentation", Proceedings of International Conference on Medical Image Computing and ComputerAssisted Intervention, Munich, Germany, October. https://doi.org/10.1007/978-3-319-24574-4_28
  17. Ruder, S. (2016), "An overview of gradient descent optimization algorithms", arXiv preprint, arXiv:1609.04747. https://doi.org/10.48550/arXiv.1609.04747
  18. See, J.E. (2012), "Visual inspection: a review of the literature", Report SAND2012-8590; Accessed on 2022.03.04. https://www.osti.gov/servlets/purl/1055636
  19. Spencer, B.F., Hoskere, V. and Narazaki, Y. (2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Engineering, 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030
  20. Wang, X. and Wu, N. (2019), "Crack identification at welding joint with a new smart coating sensor and entropy", Mech. Syst. Signal Process., 124, 65-82. https://doi.org/10.1016/j.ymssp.2019.01.044
  21. Wang, Y., Fu, Z., Ge, H., Ji, B. and Hayakawa, N. (2019), "Cracking reasons and features of fatigue details in the diaphragm of curved steel box girder", Eng. Struct., 201, 109767. https://doi.org/10.1016/j.engstruct.2019.109767
  22. Xu, Y., Bao, Y., Chen, J., Zuo, W. and Li, H. (2018), "Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images", Struct. Health Monitor., 18(3), 653-674. https://doi.org/10.1177/1475921718764873
  23. Yang, X., Li, H., Yu, Y., Luo, X., Huang, T. and Yang, X. (2018), "Automatic pixel-level crack detection and measurement using fully convolutional network", Comput.-Aided Civ. Infrastruct. Eng., 33(12), 1090-1109. https://doi.org/10.1111/mice.12412
  24. Yao, L., Dong, Q., Jiang, J. and Ni, F. (2020), "Deep reinforcement learning for long-term pavement maintenance planning", Comput.-Aided Civ. Infrastruct. Eng., 35(11), 1230-1245. https://doi.org/10.1111/mice.12558
  25. Ye, X., Jin, T. and Yun, C. (2019), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Struct. Syst., Int. J., 24 (5), 567-585. https://doi.org/10.12989/sss.2019.24.5.567
  26. Zhao, Z.Q., Zheng, P., Xu, S.T. and Wu, X. (2019), "Object detection with deep learning: a review", IEEE Trans. Neural Netw. Learn. Syst., 30(11), 3212-3232. Doi: 10.1109/TNNLS.2018.2876865
  27. Zhu, X.P., Rizzo, P., Marzani, A. and Bruck, J. (2010), "Ultrasonic guided waves for nondestructive evaluation/structural health monitoring of trusses", Meas. Sci. Technol., 21(4). https://doi.org/10.1088/0957-0233/21/4/045701