ISSN 1210-2512 (Print)

ISSN 1805-9600 (Online)

Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

About the Journal
Feature Articles
Editorial Board
Publishing Department
Society [CZ]

Log out
Your Profile
Administration

September 2023, Volume 32, Number 3 [DOI: 10.13164/re.2023-3]

Show all Hide all

D. Chen, S. Xiong, L. Guo [references] [full-text] [DOI: 10.13164/re.2023.0299] [Download Citations]
Research on Detection Method for Tunnel Lining Defects Based on DCAM-YOLOv5 in GPR B-Scan

This paper presents a detection method of DCAM-YOLOv5 for ground penetrating radar (GPR) to address the difficulty of identifying complex and multi-type defects in tunnel linings. The diversity of tunnel-lining defects and the multiple reflections and scattering caused by water-bearing defects make GPR images quite complex. Although existing methods can identify the position of underground defects from B-scans, their classification accuracy is not high. The DCAM-YOLOv5 adopts YOLOv5 as the baseline model and integrates deformable convolution and convolutional block attention module (CBAM) without adding a large number of parameters to improve the adaptive learning ability for irregular geometric shapes and boundary fuzzy defects. In this study, dielectric constant models of tunnel linings are established based on the electromagnetic simulation software (GPRMAX), including rebar and various structural defects. The simulated and field GPR B-scan images show that the DCAM-YOLOv5 method has better results for detecting different types of defects than other methods, which validates the effectiveness of the proposed detection method.

  1. JIANG, Y. J., ZHANG, X. P., TANIGUCHI, T. Quantitative condition inspection and assessment of tunnel lining. Automation in Construction, 2019, vol. 102, p. 258–269. DOI: 10.1016/j.autcon.2019.03.001
  2. YE, F., QIN, N., LIANG, X., et al. Analyses of the defects in highway tunnels in China. Tunnelling and Underground Space Technology, 2021, vol. 107, p. 1–17. DOI: 10.1016/j.tust.2020.103658
  3. TESIC, K., BARICEVIC, A., SERDAR, M. Non-destructive corrosion inspection of reinforced concrete using ground-penetrating radar: A review. Materials, 2021, vol. 14, no. 4, p. 1–20. DOI: 10.3390/ma14040975
  4. GOMEZA, J., CASAS, J. R., VILLALBA, S. Structural health monitoring with distributed optical fiber sensors of tunnel lining affected by nearby construction activity. Automation in Construction, 2020, vol. 117, p. 1–18. DOI: 10.1016/j.autcon.2020.103261
  5. MURTHY, A. R., PUKAZHENDHI, D., VISHNUVARDHAN, S., et al. Performance of concrete beams reinforced with GFRP bars under monotonic loading. Structures, 2020, vol. 27, p. 1274–1288. DOI: 10.1016/j.istruc.2020.07.020
  6. LEI, M. F., LIU, L. H., SHI, C. H., et al. A novel tunnel-lining crack recognition system based on digital image technology. Tunnelling and Underground Space Technology, 2021, vol. 108, p. 1–13. DOI: 10.1016/j.tust.2020.103724
  7. ASADI, P., GINDY, M., ALVAREZ, M., et al. A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data. Automation in Construction, 2020, vol. 112, p. 1–12. DOI: 10.1016/j.autcon.2020.103106
  8. LOUPOS, K., DOULAMIS, A. D., STENTOUMIS, C., et al. Autonomous robotic system for tunnel structural inspection and assessment. International Journal of Intelligent Robotics and Applications, 2018, vol. 2, no. 1, p. 43–66. DOI: 10.1007/s41315-017-0031-9
  9. OKAZAKI, Y., OKAZAKI, S., ASAMOTO, S., et al. Applicability of machine learning to a crack model in concrete bridges. Computer-Aided Civil and Infrastructure Engineering, 2020, vol. 35, no. 8, p. 775–792. DOI: 10.1111/mice.12532
  10. LEI, W., HOU, F., XI, J., et al. Automatic hyperbola detection and fitting in GPR B-scan image. Automation in Construction, 2019, vol. 106, p. 1–14. DOI: 10.1016/j.autcon.2019.102839
  11. TAKAHIRO, Y., TSUKASA, M. Detection and localization of manhole and joint covers in radar images by support vector machine and Hough transform. Automation in Construction, 2021, vol. 126, p. 1–12. DOI: 10.1016/j.autcon.2021.103651
  12. LEE, K. L., MOKJI, M. M. Automatic target detection in GPR images using histogram of oriented gradients (HOG). In 2nd International Conference on Electronic Design (ICED). Penang (Malaysia), 2014, p. 181–186. DOI: 10.1109/ICED.2014.7015795
  13. NOREEN, T., KHAN, U. S. Using pattern recognition with HOG to automatically detect reflection hyperbolas in ground penetrating radar data. In International Conference on Electrical and Computing Technologies and Applications (ICECTA). Ras Al Khaimah (United Arab Emirates), 2017, p. 1–6. DOI: 10.1109/ICECTA.2017.8252064
  14. MERTENS, L., PERSICO, R., MATERA, L., et al. Automated detection of reflection hyperbolas in complex GPR images with no a priori knowledge on the medium. IEEE Transactions on Geoscience and Remote Sensing, 2016, vol. 54, no. 1, p. 580–596. DOI: 10.1109/TGRS.2015.2462727
  15. PEER, U., DY, J. G. Automated target detection for geophysical applications.IEEE Transactions on Geoscience and Remote Sensing, 2017, vol. 55, no. 3, p. 1563–1572. DOI: 10.1109/TGRS.2016.2627245
  16. ZHOU, Z., ZHANG, J., GONG, C. Automatic detection method of tunnel lining multi- defects via an enhanced you only look once network. Computer-Aided Civil and Infrastructure Engineering, 2022, vol. 37, no. 6, p. 762–780. DOI: 10.1111/ mice.12836
  17. KIM, N., KIM, K., AN, Y. K., et al. Deep learning-based underground object detection for urban road pavement. International Journal of Pavement Engineering, 2020, vol. 21, no. 13, p. 1638–1650. DOI: 10.1080/10298436.2018.1559317
  18. PARK, S., KIM, J., JEON, K., et al. Improvement of GPR-based rebar diameter estimation using YOLO-v3. Remote Sensing, 2021, vol. 13, no. 10, p. 1–12. DOI: 10.3390/rs13102011
  19. YANG, J., SONG, F. B., ZHANG, P. L., et al. Application of deep learning in ground penetrating radar image recognition. In International Conference on Artificial Intelligence (CAIBDA). Xi’an (China), 2021, p. 16–20. DOI: 10.1109/CAIBDA53561.2021.00011
  20. LI, S. W., GU, X. Y., XU, X. G., et al. Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm. Construction and Building Materials, 2021, vol. 273, p. 1–14. DOI: 10.1016/j.conbuildmat.2020.121949
  21. CHEN, L. J., YAO, H. D., FU, J. Y., et al. The classification and localization of crack using lightweight convolutional neural network with CBAM. Engineering Structures, 2023, vol. 275, no. B, p. 1–16. DOI: 10.1016/j.engstruct.2022.115291
  22. HUANG, F. Q., CHEN, M., FENG, G. F. Improved YOLO object detection algorithm based on deformable convolution. (in Chinese). Computer Engineering, 2021, vol. 47, no. 10, p. 269–275, 282. DOI: 10.19678/j.issn.1000-3428.0059096
  23. KAUR, P., DANA, K. J., ROMERO, F. A., et al. Automated GPR rebar analysis for robotic bridge deck evaluation. IEEE Transactions on Cybernetics, 2015, vol. 46, no. 10, p. 2265–2276. DOI: 10.1109/TCYB.2015.2474747
  24. CHEN, I. X. Y. GPR-Data-Classifier. [Online]. Available at: https://github.com/irenexychen/gpr-data-classifier
  25. ZHOU, X. R., CHEN, H. H., LI, J. L., et al. An automatic GPR B-scan image interpreting model. IEEE Transactions on Geoscience and Remote Sensing, 2018, vol. 56, no. 6, p. 3398–3412. DOI: 10.1109/TGRS.2018.2799586
  26. WANG, X. Y., CHEN, L., BAN, T. Y., et al. Accurate label refinement from multiannotator of remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–13. DOI: 10.1109/TGRS.2023.3241402
  27. WOO, S., PARK, J., LEE, J., et al. CBAM: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV). Munich (Germany), 2018, p. 3–19. DOI: 10.1007/978-3-030-01234-2_1

Keywords: Ground penetrating radar, tunnel-lining defects, YOLOv5, deformable convolution, CBAM, GPRMAX