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Deep learning based crack detection from tunnel cement concrete lining

딥러닝 기반 터널 콘크리트 라이닝 균열 탐지

  • Bae, Soohyeon (Dept. of Geoinformatics, University of Seoul) ;
  • Ham, Sangwoo (Dept. of Geoinformatics, University of Seoul) ;
  • Lee, Impyeong (Dept. of Geoinformatics, University of Seoul) ;
  • Lee, Gyu-Phil (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Donggyou (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
  • 배수현 (서울시립대학교 대학원 공간정보공학과) ;
  • 함상우 (서울시립대학교 대학원 공간정보공학과) ;
  • 이임평 (서울시립대학교 공간정보공학과) ;
  • 이규필 (한국건설기술연구원 지반연구본부) ;
  • 김동규 (한국건설기술연구원 지반연구본부)
  • Received : 2022.09.27
  • Accepted : 2022.10.20
  • Published : 2022.11.30

Abstract

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

인력기반 터널 점검은 점검자의 주관적인 판단에 영향을 받으며 지속적인 이력관리가 어렵다. 따라서 최근에는 딥러닝 기반 자동 균열 탐지 연구가 활발히 진행되고 있다. 하지만 대부분의 연구에서는 사용하는 대규모 공개 균열 데이터셋은 터널 내부에서 발생하는 균열과 매우 상이하다. 또한 현행 터널 상태평가에서 정교한 균열 레이블을 구축하기 위해서는 추가적인 작업이 요구된다. 이에 본 연구는 균열 형상이 다소 단순하게 표현된 기존 데이터셋을 딥러닝 모델에 입력하여 균열 탐지 성능을 개선하는 방안을 제시한다. 기존 터널 데이터셋, 고품질 터널 데이터셋과 공개 균열 데이터셋을 조합하여 학습한 딥러닝 모델의 성능 평가와 비교를 수행한다. 그 결과 Cross Entropy 손실함수를 사용한 DeepLabv3+에 공개 데이터셋, 패치 단위 분류와 오버샘플링을 수행한 터널 데이터셋을 모두 학습한 경우 성능이 가장 좋았다. 향후 기 구축된 터널 영상 취득 시스템 데이터를 딥러닝 모델 학습에 효율적으로 활용하기 위한 방안을 수립하는 데 기여할 것으로 기대한다.

Keywords

Acknowledgement

본 논문은 한국건설기술연구원 주요사업으로 지원을 받아 수행된 연구(인공지능을 활용한 대심도 지하 대공간의 스마트 복합 솔루션 개발)로 이에 감사합니다.

References

  1. Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H. (2018), "Encoder-decoder with atrous separable convolution for semantic image segmentation", Proceedings of the European Conference on Computer Vision (ECCV), Munich, pp. 801-818.
  2. Chu, H., Wang, W., Deng, L. (2022), "Tiny-Crack-Net: a multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks", Computer-Aided Civil and Infrastructure Engineering, Vol. 37, No. 14, pp. 1914-1931. https://doi.org/10.1111/mice.12881
  3. Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., Gross, H.M. (2017), "How to get pavement distress detection ready for deep learning? a systematic approach", Proceedings of the 2017 International Joint Conference on Neural Networks, Anchorage, pp. 2039-2047.
  4. Hadinata, P.N., Simanta, D., Eddy, L., Nagai, K. (2021), "Crack detection on concrete surfaces using deep encoder-decoder convolutional neural network: a comparison study between U-Net and DeepLabV3+", Journal of the Civil Engineering Forum, Vol. 7, No. 3, pp. 323-334. https://doi.org/10.22146/jcef.65288
  5. Ham, S., Bae, S., Kim, H., Lee, I., Lee, G.P., Kim, D. (2021), "Training a semantic segmentation model for cracks in the concrete lining of tunnel", Journal of Korean Tunnelling and Underground Space Association, Vol. 23, No. 6, pp. 549-558. https://doi.org/10.9711/KTAJ.2021.23.6.549
  6. Han, C., Ma, T., Huyan, J., Huang, X., Zhang, Y. (2021), "CrackW-Net: a novel pavement crack image segmentation convolutional neural network", IEEE Transactions on Intelligent Transportation Systems, pp. 1-10.
  7. Han, X., Zhao, Z., Chen, L., Hu, X., Tian, Y., Zhai, C., Wang, L., Huang, X. (2022), "Structural damagecausing concrete cracking detection based on a deep-learning method", Construction and Building Material, Vol. 337, No. 27, 127562.
  8. Hsieh, Y.A., Tsai, Y.J. (2020), "Machine learning for crack detection: review and model performance comparison", Journal of Computing in Civil Engineering, Vol. 34, No. 5, 04020038.
  9. Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., Wu, J. (2020), "UNet3+: a full-scale connected UNet for medical image segmentation", Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, pp. 1055-1059.
  10. Ji, A., Xue, X., Wang, Y., Luo, X., Xue, W. (2020), "An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement", Automation in Construction, Vol. 114, 103176.
  11. Johnson, J.M., Khoshgoftaar, T.M. (2019), "Survey on deep learning with class imbalance", Journal of Big Data, Vol. 6, No. 1, pp. 1-54. https://doi.org/10.1186/s40537-018-0162-3
  12. Kaiser, P., Wegner, J.D., Lucchi, A., Jaggi, M., Hofmann, T., Schindler, K. (2017), "Learning aerial image segmentation from online maps", IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 11, pp. 6054-6068. https://doi.org/10.1109/TGRS.2017.2719738
  13. Kim, A.R., Kim, D.H., Byun, Y.S., Lee, S.W. (2018), "Crack detection of concrete structure using deep learning and image processing method in geotechnical engineering", Journal of the Korean Geotechnical Society, Vol. 34, No. 12, pp. 145-154. https://doi.org/10.7843/KGS.2018.34.12.145
  14. Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., Cho, K. (2019), "Augmentation for small object detection", arXiv:1902.07296.
  15. Li, D., Duan, Z., Hu, X., Zhang, D. (2021), "Pixel-level recognition of pavement distresses based on U-Net", Advances in Materials Science and Engineering, Vol. 2021, 5586615.
  16. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P. (2017), "Focal loss for dense object detection", Proceedings of the IEEE International Conference on Computer Vision, Venice, pp. 2980-2988.
  17. Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P. (2016), "Convolutional neural networks for large-scale remote-sensing image classification", IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 2, pp. 645-657. https://doi.org/10.1109/TGRS.2016.2612821
  18. Middha, L., Crack segmentation dataset, https://www.kaggle.com/lakshaymiddha/crack-segmentation-dataset (September 20, 2022).
  19. Rolnick, D., Veit, A., Belongie, S., Shavit, N. (2017), "Deep learning is robust to massive label noise", arXiv:1705.10694.
  20. Ronneberger, O., Fischer, P., Brox, T. (2015), "U-net: convolutional networks for biomedical image segmentation", Proceedings of the International Conference on Medical Image Computing and ComputerAssisted Intervention, Munich, pp. 234-241.
  21. Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z. (2016), "Automatic road crack detection using random structured forests", IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 12, pp. 3434-3445. https://doi.org/10.1109/TITS.2016.2552248
  22. Shim, S., Choi, S.I., Kong, S.M., Lee, S.W. (2021), "Deep learning algorithm of concrete spalling detection using focal loss and data augmentation", Journal of Korean Tunnelling and Underground Space Association, Vol. 23, No. 4, pp. 253-263. https://doi.org/10.9711/KTAJ.2021.23.4.253
  23. Wang, Z., Xu, G., Ding, Y., Wu, B., Lu, G. (2020), "A vision-based active learning convolutional neural network model for concrete surface crack detection", Advances in Structural Engineering, Vol. 23, No. 13, pp. 2952-2964. https://doi.org/10.1177/1369433220924792
  24. Zou, Q., Cao, Y., Li, Q., Mao, Q., Wang, S. (2012), "Cracktree: automatic crack detection from pavement images", Pattern Recognition Letters, Vol. 33, No. 3, pp. 227-238. https://doi.org/10.1016/j.patrec.2011.11.004
  25. Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S. (2018), "Deepcrack: learning hierarchical convolutional features for crack detection", IEEE Transactions on Image Processing, Vol. 28, No. 3, pp. 1498-1512. https://doi.org/10.1109/tip.2018.2878966