Deep learning algorithm of concrete spalling detection using focal loss and data augmentation |
Shim, Seungbo
(Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Choi, Sang-Il (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) Kong, Suk-Min (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) Lee, Seong-Won (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) |
1 | Hoang, N.D., Nguyen, Q.L., Tran, X.L. (2019). "Automatic detection of concrete spalling using piecewise linear stochastic gradient descent logistic regression and image texture analysis", Complexity, Vol. 2019. |
2 | Kim, M.K., Sohn, H., Chang, C.C. (2015), "Localization and quantification of concrete spalling defects using terrestrial laser scanning", Journal of Computing in Civil Engineering, Vol. 29, No. 6, p. 04014086. DOI |
3 | 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, Italy, pp. 2980-2988. |
4 | Shim, S., Kim, J., Cho, G.C., Lee, S.W. (2020), "Multiscale and adversarial learning-based semi-supervised semantic segmentation approach for crack detection in concrete structures", IEEE Access, Vol. 8, pp. 170939-170950. DOI |
5 | Wu, H., Ao, X., Chen, Z., Liu, C., Xu, Z., Yu, P. (2019), "Concrete spalling detection for metro tunnel from point cloud based on roughness descriptor", Journal of Sensors, Vol. 2019. |
6 | Zhang, W., Zhang, Z., Qi, D., Liu, Y. (2014), "Automatic crack detection and classification method for subway tunnel safety monitoring", Sensors, Vol. 14, No. 10, pp. 19307-19328. DOI |
7 | Fujino, Y., Siringoringo, D.M. (2020), "Recent research and development programs for infrastructures maintenance, renovation and management in Japan", Structure and Infrastructure Engineering, Vol. 16, No. 1, pp. 3-25. DOI |
8 | German, S., Brilakis, I., DesRoches, R. (2012), "Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments", Advanced Engineering Informatics, Vol. 26, No. 4, pp. 846-858. DOI |
9 | Hoang, N.D. (2018), "Image processing-based recognition of wall defects using machine learning approaches and steerable filters", Computational Intelligence and Neuroscience, Vol. 2018. |
10 | Li, S., Zhao, X., Zhou, G. (2019), "Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network", Computer-Aided Civil and Infrastructure Engineering, Vol. 34, No. 7, pp. 616-634. DOI |
11 | Nishikawa, T., Yoshida, J., Sugiyama, T., Fujino, Y. (2012), "Concrete crack detection by multiple sequential image filtering", Computer-Aided Civil and Infrastructure Engineering, Vol. 27, No. 1, pp. 29-47. DOI |
12 | Yang, L., Li, B., Li, W., Liu, Z., Yang, G., Xiao, J. (2017), "Deep concrete inspection using unmanned aerial vehicle towards CSSC database", Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vancouver, Canada, pp. 24-28. |
13 | Fujita, Y., Hamamoto, Y. (2011), "A robust automatic crack detection method from noisy concrete surfaces", Machine Vision and Applications, Vol. 22, No. 2, pp. 245-254. DOI |
14 | Liu, Z., Cao, Y., Wang, Y., Wang, W. (2019), "Computer vision-based concrete crack detection using U-net fully convolutional networks", Automation in Construction, Vol. 104, pp. 129-139. DOI |
15 | Dawood, T., Zhu, Z., Zayed, T. (2017), "Machine vision-based model for spalling detection and quantification in subway networks", Automation in Construction, Vol. 81, pp. 149-160. DOI |
16 | Dung, C.V., Anh, L.D. (2019), "Autonomous concrete crack detection using deep fully convolutional neural network", Automation in Construction, Vol. 99, pp. 52-58. DOI |