Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Loop closure-based high-resolution façade digital modeling technique of large-scale dams using UAV

  • Myung Soo Kang (Department of Architectural Engineering, Sejong University) ;
  • Keunyoung Jang (Department of Architectural Engineering, Sejong University) ;
  • Yong-Rae Yu (Department of Civil and Environmental Engineering, Sejong University) ;
  • Yun-Kyu An (Department of Architectural Engineering, Sejong University)
  • Received : 2023.11.20
  • Accepted : 2024.05.13
  • Published : 2024.05.25
⟨ Previous Next ⟩

Abstract

Structural digital models can be effectively established by spatially obtaining digital images using an unmanned aerial vehicle (UAV). One of the main purposes of the structural digital modeling is computer vision-based exterior damage detection of a target structure. To investigate micro-scale damage from the digital model, high-resolution digital images obtained with a close-up vision survey is typically required. However, serial image synthesis such as image stitching may cumulate stitching errors as the number of scanned images increases. Therefore, in this paper, a novel loop closure-based digital image stitching technique is proposed and experimentally validated using the close-up surveyed digital images acquired from an in-situ dam structure located in South Korea. The test results reveal that the proposed technique outperforms a non-loop closure-based image stitching technique, which can cause serious distortions, such as ghosting and vanishing phenomena.

Keywords

Acknowledgement

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research, No.412024115147) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).

References

  1. 4DiXplorer (2021), Available online: www.4dixplorer.com/software_satpp.html (accessed on 03 November 2021).
  2. Agisoft Meta shape (2021), Available online: www.agisoft.com (accessed on 03 November 2021).
  3. Bang, S., Kim, H. and Kim, H. (2017), "UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching", Automat. Constr., 84, 70-80. https://doi.org/10.1016/j.autcon.2017.08.031
  4. Boller, C., Starke, P., Dobmann, G., Kuo, C.-M. and Kuo, C.H. (2015), "Approaching the assessment of ageing bridge infrastructure", Smart Struct. Syst., Int. J., 15(3), 593-608. https://doi.org/10.12989/sss.2015.15.3.593
  5. Brown, M. and Lowe, D.G. (2006), "Automatic panoramic image stitching using invariant features", Int. J. Comput. Vis., 74(1), 59-73. https://doi.org/10.1007/s11263-006-0002-3
  6. Cavalagli, N., Gioffre, M., Grassi, S., Gusella, V., Pepi, C. and Volpi, G.M. (2020), "On the accuracy of UAV photogrammetric survey for the evaluation of historic masonry structural damages", Procedia Struct. Integr., 29, 165-174. https://doi.org/10.1016/j.prostr.2020.11.153
  7. Chen, Y.S. and Chuang, Y.Y. (2016), "Natural image stitching with global similarity prior", Proceedings of European Conference on Computer Vision (ECCV 2016), Amsterdam, Netherlands, October, pp. 186-201. https://doi.org/10.1007/978-3-319-46454-1_12
  8. Chen, S., Laefer, D.F., Mangina, E., Zolanvari, S.M.I. and Byrne, J. (2019), "UAV bridge inspection through evaluated 3D reconstructions", J. Bridge Eng., 24(4), 1-15. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001343
  9. Cheng, K., Shan, J. and Liu, Y. (2021), "Feature-based image stitching for panorama construction and visual inspection of structures", Smart Struct. Syst., Int. J., 28(5), 661-673. https://doi.org/10.12989/sss.2021.28.5.661
  10. Chiang, K.W., Tsai, G.-J., Li, Y.H. and El-Sheimy, N. (2017), "Development of LiDAR-based UAV system for environment reconstruction", IEEE Geosci. Remote Sens. Lett., 14(10), 1790-1794. https://doi.org/10.1109/LGRS.2017.2736013
  11. Dabous, S.A., Al-Ruzouq, R. and Llort, D. (2023), "Three-dimensional modeling and defect quantification of existing concrete bridges based on photogrammetry and computer aided design", Ain Shams Eng. J., 14(12), 102231. https://doi.org/10.1016/j.asej.2023.102231
  12. Drescek, U., Kosmatin Fras, M., Tekavec, J. and Lisec, A. (2020), "Spatial ETL for 3D building modelling based on unmanned aerial vehicle data in semi-urban areas", Remote Sens., 12(12), 1-24. https://doi.org/10.3390/rs12121972
  13. Furukawa, Y. and Ponce, J. (2010), "Accurate, dense, and robust multiview stereopsis", IEEE Trans. Pattern Anal. Mach. Intell., 32(8), 1362-1376. https://doi.org/10.1109/TPAMI.2009.161
  14. Jang, K., An, Y.-K., Kim, B. and Cho, S. (2021), "Automated crack evaluation of a high-rise bridge pier using a ring-type climbing robot", Comput.-Aided Civ. Infrastruct. Eng., 36(1), 14-29. https://doi.org/10.1111/mice.12550
  15. Jang, K., Park, S., Jung H., Yoo H. and An, Y.-K. (2024), "Deep learning-based 3D digital damage map of vertical-type tunnels using unmanned fusion data scannning", Automat. Constr., 162, 1-13. https://doi.org/10.1016/j.autcon.2024.105397
  16. Jhan, J.P., Kerle, N. and Rau, J.Y. (2021), "Integrating UAV and ground panoramic images for point cloud analysis of damaged building", IEEE Geosci. and Remote Sens. Lett., 19, 1-5. https://doi.org/10.1109/LGRS.2020.3048150
  17. Jiang, W., Zhou, Y., Ding, L., Zhou, C. and Ning, X. (2020), "UAV-based 3D reconstruction for hoist mapping and layout planning in petrochemical construction", Automat. Constr., 113, 1-12. https://doi.org/10.1016/j.autcon.2020.103137
  18. Kang, M.S. and An, Y.-K. (2021), "Deep learning-based automated background removal for structural exterior image stitching", Appl. Sci., 11(8), 1-12. https://doi.org/10.3390/app11083339
  19. Khaloo, A., Lattanzi, D., Cunningham, K., Dell'Andrea, R. and Riley, M. (2018), "Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modeling", Struct. Infrastruct. Eng., 14(1), 124-136. https://doi.org/10.1080/15732479.2017.1330891
  20. Lowe, D.G. (1999), "Object recognition from local scale-invariant features", Proceedings of the International Conference on Computer Vision (ICCV 1999), Corfu, Greece, September, pp. 1150-1157. https://doi.org/10.1109/ICCV.1999.790410
  21. Murtiyoso, A. and Grussenmeyer, P. (2017), "Documentation of heritage buildings using close-range UAV images: dense matching issues, comparison and case studies", The Photogram. Rec., 32(159), 206-229. https://doi.org/10.1111/phor.12197
  22. Pan, Y., Dong, Y., Wang, D., Chen, A. and Ye, Z. (2019), "Three-dimensional reconstruction of structural surface model of heritage bridges using UAV-based photogrammetric point clouds", Remote Sens., 11(10), 1-20. https://doi.org/10.3390/rs11101204
  23. Schonberger, J.L. (2021), ColMap, Available online: https://colmap.github.io (accessed on 03 November 2021).
  24. Shen, S. (2013), "Accurate multiple view 3D reconstruction using patch-based stereo for large-scale scenes", IEEE Trans. Image Process., 22(5), 1901-1914. https://doi.org/10.1109/TIP.2013.2237921
  25. Snavely, N., Seitz, S.M. and Szeliski, R. (2008), "Modeling the world from internet photo collections", Int. J. Comput. Vis., 80(2), 189-210. https://doi.org/10.1007/s11263-007-0107-3
  26. Szeliski, R. (2006), "Image alignment and stitching: A tutorial", Int. J. Comput. Vis., 2(1), 1-104. http://dx.doi.org/10.1561/0600000009
  27. Varbla, S., Puust, R. and Ellmann, A. (2020), "Accuracy assessment of RTK-GNSS equipped UAV conducted as-built surveys for construction site modelling", Surv. Rev., 53(381), 477-492. https://doi.org/10.1080/00396265.2020.1830544
  28. Vosselman, G. and Dijkman, S. (2001), "3D building model reconstruction from point clouds and ground plans", Int. Arch. Photogramm, Remote Sens., 34(3/W4), 37-43.
  29. Wang, D., Zhang, Y., Pan, Y., Peng, B., Liu, H. and Ma, R. (2020), "An automated inspection method for the steel box girder bottom of long-span bridges based on deep learning", IEEE Access, 8, 94010-94023. https://doi.org/10.1109/ACCESS.2020.2994275
  30. Wang, L., Spencer, B.F., Li, J. and Hu, P. (2021), "A fast image-stitching algorithm for characterization of cracks in large-scale structures", Smart Struct. Syst., Int. J., 27(4), 593-605. https://doi.org/10.12989/sss.2021.27.4.593
  31. Wu, B., Yu, B., Wu, Q., Yao, S., Zhao, F., Mao, W. and Wu, J. (2017), "A graph-based approach for 3D building model reconstruction from airborne LiDAR point clouds", Remote Sens., 9(1), 1-16. https://doi.org/10.3390/rs9010092
  32. Zaragoza, J., Chin, T.J., Brown, M.S. and Suter, D. (2013), "As-projective-as-possible image stitching with moving DLT", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), Portland, OR, USA, June, pp. 2339-2346.
  33. Zhao, S., Kang, F., Li, J. and Ma, C. (2021), "Concrete dam damage detection and localization based on YOLOv5s-HSC and photogrammetric 3D reconstruction", Automat. Constr., 130, 1-16. https://doi.org/10.1016/j.autcon.2022.104555
  34. Zhao, S., Kang, F. and Li, J. (2022), "Concrete dam damage detection and localization based on YOLOv5s-HSC and photogrammetric 3D reconstruction", Automat. Constr., 143, 1-18. https://doi.org/10.1016/j.autcon.2022.104555
  35. Zollini, S., Alicandro, M., Dominici, D., Quaresima, R. and Giallonardo, M. (2020), "UAV photogrammetry for concrete bridge inspection using object-based image analysis (OBIA)", Remote Sens., 12(19), 1-16. https://doi.org/10.3390/rs12193180