Deep Learning-Based Classification of Defective Tunnel Linings

References

1. J.Wang, K.Chen, H.Liu, J.Zhang, W.Kang, S.Li, P.Jiang, Q.Sui, and Z.Wang, “Deep Learning-Based Rebar Clutters Removal and Defect Echoes Enhancement in GPR Images,” IEEE Access, vol. 9, pp. 87207–87218, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3088630.
   [Google Scholar]
2. Y.Wang, H.Qin, Y.Tang, D.Zhang, D.Yang, C.Qu, and T.Geng, “RCE-Gan: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images,” Remote Sensing, vol. 14, no. 2, p. 251, 2022, doi: https://doi.org/10.3390/rs14020251.
   [Google Scholar]
3. Z.Wang, J.Wang, K.Chen, Z.Li, J.Xu, Y.Li, and Q.Sui, “Unsupervised Learning Method for Rebar Signal Suppression and Defect Signal Reconstruction and Detection in Ground Penetrating Radar Images,” Measurement, vol. 211, p. 112652, 2023/04/01/ 2023, doi:https://doi.org/10.1016/j.measurement.2023.112652.
   [Google Scholar]
4. Q.Ren, Y.Wang, J.Xu, F.Hou, G.Cui, & G.Ding (2024). REN-GAN: Generative adversarial network-driven rebar clutter elimination network in GPR image for tunnel defect identification. Expert Systems with Applications, 255, 124395.
   [Google Scholar]
5. X.Wang, H.Liu, X.Meng, J.Cui, and Y.Du, “Enhanced Imaging of Concealed Defects Behind Concrete Linings Using Residual Channel Attention Network for Rebar Clutter Suppression,” Automation in Construction, vol. 166, p. 105574, 2024, doi:https://doi.org/10.1016/j.autcon.2024.105574.
   [Google Scholar]
6. M.Tan and Q.Le, “EfficientNetV2: Smaller Models and Faster Training,” presented at the Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research, Virtual, vol. 139, pp. 10096–10106, 2021. doi:https://doi.org/10.48550/arXiv.2104.00298.
   [Google Scholar]
7. M.Guo, T.Xu, J.Liu, Z.Liu, P.Jiang, T.Mu, S.Zhang, R.Martin, M.Cheng, and S.Hu, “Attention Mechanisms in Computer Vision: A Survey,” Computational visual media, vol. 8, no. 3, pp. 331–368, 2022. doi: https://doi.org/10.1007/s41095-022-0271-y.
   [Google Scholar]