School of Materials Science and Engineering
Chongqing Jiaotong University
China
Homepage
School of Materials Science and Engineering
Chongqing Jiaotong University
China
Homepage
School of Materials Science and Engineering
Chongqing Jiaotong University
China
Homepage1. Hu, J. Y., S. S. Zhang, E. Chen, and W. G. Li, "A review on corrosion detection and protection of existing reinforced concrete (RC) structures," Construction and Building Materials, Vol. 325, 126718, 2022.
2. Han, Xiaofeng, Gege Li, Penggang Wang, Zhaoyi Chen, Dongbo Cui, Hai Zhang, Li Tian, Xiangming Zhou, Zuquan Jin, and Tiejun Zhao, "A new method and device for detecting rebars in concrete based on capacitance," Measurement, Vol. 202, 111721, 2022.
3. Goffin, Brigitte, Nemkumar Banthia, and Noboru Yonemitsu, "Use of infrared thermal imaging to detect corrosion of epoxy coated and uncoated rebar in concrete," Construction and Building Materials, Vol. 263, 120162, 2020.
4. Chen, Ruoyu, Khiem T. Tran, Hung Manh La, Taylor Rawlinson, and Kien Dinh, "Detection of delamination and rebar debonding in concrete structures with ultrasonic SH-waveform tomography," Automation in Construction, Vol. 133, 104004, 2022.
5. Ham, Suyun and John S. Popovics, "Application of contactless ultrasound toward automated inspection of concrete structures," Automation in Construction, Vol. 58, 155-164, 2015.
6. Ichi, Eberechi and Sattar Dorafshan, "Effectiveness of infrared thermography for delamination detection in reinforced concrete bridge decks," Automation in Construction, Vol. 142, 104523, 2022.
7. Hwang, Soonkyu, Hyeonjin Kim, Hyung Jin Lim, Peipei Liu, and Hoon Sohn, "Automated visualization of steel structure coating thickness using line laser scanning thermography," Automation in Construction, Vol. 139, 104267, 2022.
8. Ghosh, Rishav, Mukund Lahoti, and Bhoomi Shah, "A succinct review on the use of NMR spectroscopy in monitoring hydration, strength development, and inspection of concrete," Materials Today: Proceedings, Vol. 61, 167-173, 2022.
9. Rubinacci, Guglielmo, Antonello Tamburrino, and Salvatore Ventre, "Concrete rebars inspection by eddy current testing," International Journal of Applied Electromagnetics and Mechanics, Vol. 25, No. 1-4, 333-339, 2007.
10. Pozzer, Sandra, Marcos Paulo Vieira De Souza, Bata Hena, Setayesh Hesam, Reza Khoshkbary Rezayiye, Ehsan Rezazadeh Azar, Fernando Lopez, and Xavier Maldague, "Effect of different imaging modalities on the performance of a CNN: An experimental study on damage segmentation in infrared, visible, and fused images of concrete structures," NDT & E International, Vol. 132, 102709, 2022.
11. Kasai, Naoya, Kouichi Sekino, Seishu Miyazaki, et al., "An eddy current convergence probe with copper core and single detection coil to detect flaws on aluminum plates," NDT & E International, Vol. 132, 102707, 2022.
12. Chen, Haitao, Leng Liao, Jianting Zhou, Hong Zhang, Senhua Zhang, Tian Lan, Zhengren Zhang, and Chunlian Hu, "Magnetic resonance eddy penetrating imaging for detecting reinforcement corrosion in concrete," Automation in Construction, Vol. 165, 105512, 2024.
13. Li, Xiaofeng, Hai Liu, Feng Zhou, Zhongchang Chen, Iraklis Giannakis, and Evert Slob, "Deep learning-based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data," Computer-Aided Civil and Infrastructure Engineering, Vol. 37, No. 14, 1834-1853, 2022.
14. Feng, Chu-Qiao, Bao-Luo Li, Yu-Fei Liu, Fu Zhang, Yan Yue, and Jian-Sheng Fan, "Crack assessment using multi-sensor fusion simultaneous localization and mapping (SLAM) and image super-resolution for bridge inspection," Automation in Construction, Vol. 155, 105047, 2023.
15. Wang, Xiangyu, Hai Liu, Xu Meng, Jie Cui, and Yanliang Du, "Enhanced imaging of concealed defects behind concrete linings using Residual Channel attention network for rebar clutter suppression," Automation in Construction, Vol. 166, 105574, 2024.
16. Jeon, Dongho, Min Kyoung Kim, Yeounung Jeong, Jae Eun Oh, Juhyuk Moon, Dong Joo Kim, and Seyoon Yoon, "High-accuracy rebar position detection using deep learning-based frequency-difference electrical resistance tomography," Automation in Construction, Vol. 135, 104116, 2022.
17. Wang, Zhengfang, Jing Wang, Kefu Chen, Zhenpeng Li, Jing Xu, Yao Li, and Qingmei Sui, "Unsupervised learning method for rebar signal suppression and defect signal reconstruction and detection in ground penetrating radar images," Measurement, Vol. 211, 112652, 2023.
18. Nguyen, Nam Hoang, "U-net based skeletonization and bag of tricks," Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2105-2109, Montreal, BC, Canada, Oct. 2021.
19. Kim, Ki-Hwan, Pyoung-Seop Shim, and Seoleun Shin, "An alternative bilinear interpolation method between spherical grids," Atmosphere, Vol. 10, No. 3, 123, 2019.
20. Cordonnier, Jean-Baptiste, Andreas Loukas, and Martin Jaggi, "Multi-head attention: Collaborate instead of concatenate," arXiv preprint arXiv:2006.16362, 2020.
21. Woo, Sanghyun, Jongchan Park, Joon-Young Lee, and In So Kweon, "CBAM: Convolutional block attention module," Proceedings of the European Conference on Computer Vision (ECCV), 3-19, Munich, Germany, Sep. 2018.
22. Qin, Ruoxi, Kai Qiao, Linyuan Wang, Lei Zeng, Jian Chen, and Bin Yan, "Weighted focal loss: An effective loss function to overcome unbalance problem of chest X-ray14," IOP Conference Series: Materials Science and Engineering, Vol. 428, No. 1, 012022, Chengdu, China, Jul. 2018.
23. Zhao, Rongjian, Buyue Qian, Xianli Zhang, Yang Li, Rong Wei, Yang Liu, and Yinggang Pan, "Rethinking dice loss for medical image segmentation," 2020 IEEE International Conference on Data Mining (ICDM), 851-860, Sorrento, Italy, Nov. 2020.
24. Liebel, Lukas and Marco Körner, "Auxiliary tasks in multi-task learning," arXiv preprint arXiv:1805.06334, 2018.
25. Lu, Xiaohu, Jian Yao, Kai Li, and Li Li, "Cannylines: A parameter-free line segment detector," 2015 IEEE International Conference on Image Processing (ICIP), 507-511, Quebec City, QC, Canada, Sep. 2015.
26. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox, "U-Net: Convolutional networks for biomedical image segmentation," Medical Image Computing and Computer-Assisted Intervention --- MICCAI 2015, 234-241, Munich, Germany, Oct. 2015.
