An evaluation methodology for cement concrete lining crack segmentation deep learning model |
Ham, Sangwoo
(Dept. of Geoinformatics, University of Seoul)
Bae, Soohyeon (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) |
1 | 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. DOI |
2 | Kim, A.R., Kim, D., 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. DOI |
3 | Liu, Y., Yao, J., Lu, X., Xie, R., Li, L. (2019), "DeepCrack: A deep hierarchical feature learning architecture for crack segmentation", Neurocomputing, Vol. 338, pp. 139-153. DOI |
4 | MMSegmentation Contributors (2020), "OpenMMLab semantic segmentation toolbox and benchmark", https://github.com/open-mmlab/mmsegmentation (September 20, 2022). |
5 | Mundt, M., Majumder, S., Murali, S., Panetsos, P., Ramesh, V. (2019), "Meta-learning convolutional neural architectures for multi-target concrete defect classification with the concrate defect bridge image dataset", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, California, pp. 11196-11205. |
6 | Park, G. (2022), "Building crack detection images", AI Hub, https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=162 (September 20, 2022). |
7 | Tsai, Y.C., Chatterjee, A. (2017), "Comprehensive, quantitative crack detection algorithm performance evaluation system", Journal of Computing in Civil Engineering, Vol. 31, No. 5, 04017047. |
8 | Zhang, T.Y., Suen, C.Y. (1984), "A fast parallel algorithm for thinning digital patterns", Communications of the ACM, Vol. 27, No. 3, pp.236-239. DOI |
9 | Paik, S.H., Choi, D.Y., Kim, Y.K., Jung, S.W., Kim, D.N. (2021), "Implementation of the drones with deep-learning crack detection analysis for inspection of bridge", The Journal of Korean Institute of Information Technology, Vol. 19, No. 3, pp. 45-52. |
10 | Scikit-image contributors (2022), "Morphological filtering", Scikit-image, https://scikit-image.org/docs/stable/auto_examples/applications/plot_morphology.html (September 20, 2022). |
11 | Garcia-Garcia, A., Orts-Escolano, S., Oprea, S.O., Villena-Martinez, V., Garcia-Rodriguez, J. (2017), "A review on deep learning techniques applied to semantic segmentation", arXiv:1704:06857, pp. 1-22. |
12 | Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H. (2018), "Encoder-decoder with atrous separable convolution for semantic image segmentation", arXiv:1802:02611, pp. 1-23. |
13 | Bianchi, E., Abbott, A.L., Tokekar, P., Hebdon, M. (2021), "COCO-Bridge: structural detail data set for bridge inspections", Journal of Computing in Civil Engineering, Vol. 35, No. 3, 04021003. |
14 | 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. |
15 | Middha, L. (2020), "Crack segmentation dataset", https://www.kaggle.com/lakshaymiddha/crack-segmen tation-dataset (September 20, 2022). |
16 | OurBrain (2022), "Big Eye System", OurBrain, http://ourbrain.co.kr/ (September 20, 2022). |