Open Access
E3S Web Conf.
Volume 205, 2020
2nd International Conference on Energy Geotechnics (ICEGT 2020)
Article Number 03007
Number of page(s) 5
Section Hydraulic Fracturing and Unconventional Hydrocarbons
Published online 18 November 2020
  1. Y. Fujita, Y. Mitani, Y. Hamamoto, A method for crack detection on a concrete structure, 18th Int. Conf. Pattern Recognit. 901-904, (2006) [Google Scholar]
  2. Y. Fujita, Y. Hamamoto, A robust automatic crack detection method from noisy concrete surfaces, Mach. Vis. Appl. 22, 245-254, (2011) [Google Scholar]
  3. I. Abdel-Qader, O. Abudayyeh, M.E. Kelly, Analysis of edge-detection techniques for crack identification in bridges, J. Comput. Civ. Eng. 17, 255-263, (2003) [CrossRef] [Google Scholar]
  4. Q. Li, Q. Zou, D. Zhang, Q. Mao, FoSA: f* seed-growing approach for crack-line detection from pavement images, Image Vis. Comput. 29, 861-872, (2011) [Google Scholar]
  5. M.R. Jahanshahi, S.F. Masri, Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures, Autom. Constr. 22, 567-576, (2012) [CrossRef] [Google Scholar]
  6. M. O’Byrne, B. Ghosh, F. Schoefs, V. Pakrashi, Regionally enhanced multiphase segmentation technique for damaged surfaces, Comput. Civ. Infrastruct. Eng. 29, 9, 644-658, (2014) [CrossRef] [Google Scholar]
  7. D. Ai, G. Jiang, L.S. Kei, C. Li, Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods, IEEE Access, 6, (2018) [Google Scholar]
  8. N.D. Hoang, Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach, Adv. Eng. Inform, 40, 110-120, (2019) [CrossRef] [Google Scholar]
  9. A. Zhang, K.C.P. Wang, B. Li, E. Yang, X. Dai, Y. Peng, Y. Fei, Y. Liu, J.Q. Li, C. Chen, Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network, Comput. Civ. Infrastruct. Eng. 32, 805-819, (2017) [CrossRef] [Google Scholar]
  10. Y.J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, Autonomous structural visual inspection using region-based deep learning for detecting multiple damage type, Comput. Civ. Infrastruct. Eng. 33, 731-747, (2018) [CrossRef] [Google Scholar]
  11. X. Yang, H. Li, Y. Yu, X. Luo, T. Huang, X. Yang, Automatic pixel-level crack detection and measurement using fully convolutional network, Comput. Civ. Infrastruct. Eng. 33, 1090-1109, (2018) [CrossRef] [Google Scholar]
  12. S. Li, X. Zhao, G. Zhou, Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network, Comput. Civ. Infrastruct. Eng. 34, 616-634, (2019) [CrossRef] [Google Scholar]
  13. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, 2016 IEEE Conf. Comput. Vis. Pattern Recognit., 770-778, (2016) [Google Scholar]
  14. S.J. Ha, J. Choo, T.S. Yun, Liquid CO2 fracturing: effect of fluid permeation on the breakdown pressure and cracking behaviour, Rock Mech. Rock Eng. 51, 3407-3420, (2018) [Google Scholar]
  15. I. Sutskever, O. Vinyals, Q.V. Le, Sequence to Sequences Learning with Neural Networks, ArXiv, (2014) [Google Scholar]
  16. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning phrase representations using RNN encoder-decoder for statistical machine translation, ArXiv, (2014) [Google Scholar]
  17. M. Teichmann, M. Weber, M. Zoellner, R. Cipolla, R. Urtasun, MultiNet: real-time joint semantic reasoning for autonomous driving, 2018 IEEE Intell. Veh. Symp., 1013-1020, (2018) [Google Scholar]
  18. V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: a deep convolutional encoder-decoder architecture for image segmentation, 2017 IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481-2495, (2017) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.