Open Access
MATEC Web Conf.
Volume 364, 2022
International Conference on Concrete Repair, Rehabilitation and Retrofitting (ICCRRR 2022)
Article Number 05020
Number of page(s) 7
Section Fibre Reinforced Cementitious Materials
Published online 07 October 2022
  1. D. Zhang, Q. Li, Y. Chen, M. Cao, L. He, and B. Zhang, An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection. Image and Vision Computing, 57, pp. 130–146 (2016). [Google Scholar]
  2. T. Nishikawa, J. Yoshida, T. Sugiyama, and Y. Fujino, “Concrete crack detection by multiple sequential image filtering,” Computer-Aided Civil and Infrastructure Engineering, vol. 27, no. 1, pp. 29–47, 2012. [CrossRef] [Google Scholar]
  3. H. Oliveira, P. Correia, Automatic road crack segmentation using entropy and image dynamic thresholding. Proc. of IEEE Signal Processing Conf., Taipei. pp. 622-626, (2009) [Google Scholar]
  4. B. Santhi, G. Krishnamurthy, S. Siddharth, P. Ramakrishnan, Automatic detection of cracks in pavements using edge detection operator. Jnl. of theoretical and Applied Information Technology, 36(2), pp. 199-205 (2012). [Google Scholar]
  5. S. W. Liu, J. H. Huang, J. C. Sung, and C. C. Lee, Detection of cracks using neural networks and computational mechanics. Computer Methods in Applied Mechanics and Engineering, 191(25-26), pp. 2831–2845 (2002). [CrossRef] [Google Scholar]
  6. M. S. Kaseko, Z. P. Lo, and S. G. Ritchie, Comparison of Traditional and neural classifiers for pavement-crack detection, Jnl. of Transportation Engineering, 120(4), pp. 552–569 (1994). [CrossRef] [Google Scholar]
  7. Y.-J. Cha, W. Choi, and O. Buyukozturk, Deep learning based crack damage detection using convolutional neural networks, Computer-Aided Civil and Infrastructure Engineering, 32(5), pp. 361–378 (2017). [CrossRef] [Google Scholar]
  8. Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, 521 (7553), pp. 436–444 (2015). [Google Scholar]
  9. Z. Qu, F. R. Ju, Y. Guo et al., Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods, PloS One, 13(7), Article IDe0201109 (2018). [Google Scholar]
  10. M.M. Khani, S. Vahidnia, Leila Ghasemzadeh, Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines. Structural Health Monitoring 19(5), (2019) [Google Scholar]
  11. A. Garcia-Garcia, S. Orts-Escolano, S.O. Oprea, V. Villena-Martinez, J. Garcia-Rodriguez, A Review on deep learning techniques applied to semantic segmentation. arXiv:1704.06857v1 [cs.CV] (2017). [Google Scholar]
  12. J. Long, E. Shelhamer, T. Darrell, Fully Convolutional Networks for Semantic Segmentation. CVPR Open Access version: (2015). [Google Scholar]
  13. C.V. Dung, L.D. Anh, Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction. 99, pp 52-58 (2019). [CrossRef] [Google Scholar]
  14. Y. Liu, S. Cho, B.F. Spencer, Automated assessment of cracks on concrete surfaces using adaptive digital image processing. Smart Struct Syst; 14(4), pp. 719–741, (2014). [CrossRef] [Google Scholar]
  15. S. Li, X. Zhao, G. Zhou, Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering. 34(7), pp. 616-634, (2019). [CrossRef] [Google Scholar]
  16. V. Hoskere, Y. Narazaki, T.A. Hoang, B.F. Spencer Jr, MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure. Journal of Civil Structural Health Monitoring, 10, pp.757–773 (2020). [CrossRef] [Google Scholar]
  17. N. Otsu. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man. Cybern, pp. 62-66. (1979). [CrossRef] [Google Scholar]
  18. J.J. Kim, A-R. Kim, S-W. Lee, Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures. Appl. Sci., 10(22), (2020); [Google Scholar]
  19. F. Yu, W. Sun, J. Li, Y. Zhao, Y. Zhang, G. Chen, An improved Otsu method for oil spill detection from SAR images. Oceanologia. 59(3), pp. 311-317 (2017). [CrossRef] [Google Scholar]
  20. N-D. Hoang, Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding. Advances in Civil Engineering. |Article ID 3924120 | (2018). [Google Scholar]
  21. D. Wilson, T. Martinez, The need for small learning rates on large problems, Proc. of 2001 International Joint Conference on Neural Networks (IJCNN’01), pp. 115–119, IEEE, Washington, (2001). [Google Scholar]

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