A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (2024)

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  • Weidong Wang Department of Civil Engineering Central South University Hunan China Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China

    Department of Civil Engineering Central South University Hunan China

    Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China

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  • Jun Peng Department of Civil Engineering Central South University Hunan China Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China

    Department of Civil Engineering Central South University Hunan China

    Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China

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    ,
  • Wenbo Hu Department of Civil Engineering Central South University Hunan China

    Department of Civil Engineering Central South University Hunan China

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    ,
  • Jin Wang Department of Civil Engineering Central South University Hunan China

    Department of Civil Engineering Central South University Hunan China

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    ,
  • Xinyue Xu Department of Civil Engineering Central South University Hunan China

    Department of Civil Engineering Central South University Hunan China

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    ,
  • Qasim Zaheer Department of Civil Engineering Central South University Hunan China

    Department of Civil Engineering Central South University Hunan China

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    ,
  • Shi Qiu Department of Civil Engineering Central South University Hunan China Department of Transportation Guangxi Provincial Government Guangxi China

    Department of Civil Engineering Central South University Hunan China

    Department of Transportation Guangxi Provincial Government Guangxi China

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Computer-Aided Civil and Infrastructure EngineeringVolume 39Issue 131 July 2024pp 2010–2027https://doi.org/10.1111/mice.13173

Published:23 February 2024Publication History

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Computer-Aided Civil and Infrastructure Engineering

Volume 39, Issue 13

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A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (1)

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Abstract

Abstract

Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1mm when the measurement distance is less than 40m. When the measurement distance is equal to 90m, the measurement error is less than 5mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.

REFERENCES

  1. Aslan G., Cakir Z., Ergintav S., Lasserre C., & Renard F. (2018). Analysis of secular ground motions in Istanbul from a long‐term InSAR time‐series (1992–2017). Remote Sensing, 10(3), 408.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (2)
  2. Azimbeik K., Hossein Mahdavi S., & Rahimzadeh Rofooei F. (2023). Improved image‐based, full‐field structural displacement measurement using template matching and camera calibration methods. Measurement: Journal of the International Measurement Confederation, 211, 112650.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (3)
  3. Bay H., Ess A., Tuytelaars T., & Van Gool L. (2008). Speeded‐up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346359.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (4)Digital Library
  4. Beauchemin S. S., & Barron J. L. (1995). The computation of optical flow. ACM Computing Surveys (CSUR), 27(3), 433466.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (6)Digital Library
  5. Benoit L., Briole P., Martin O., Thom C., Malet J. P., & Ulrich P. (2015). Monitoring landslide displacements with the Geocube wireless network of low‐cost GPS. Engineering Geology, 195, 111121.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (8)
  6. Briechle K., & Hanebeck U. D. (2001). Template matching using fast normalized cross correlation. In Casasent D. P. & Chao T.‐H. (Eds.), Optical pattern recognition XII (Vol. 4387, pp. 95102). SPIE.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (9)Cross Ref
  7. Chen J. G., Wadhwa N., Cha Y. J., Durand F., Freeman W. T., & Buyukozturk O. (2015). Modal identification of simple structures with high‐speed video using motion magnification. Journal of Sound and Vibration, 345, 5871.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (11)Cross Ref
  8. Chen L. C., Zhu Y., Papandreou G., Schroff F., & Adam H. (2018). Encoder‐decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany (pp. 801808).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (13)
  9. Dong C. Z., Ye X. W., & Jin T. (2018). Identification of structural dynamic characteristics based on machine vision technology. Measurement: Journal of the International Measurement Confederation, 126, 405416.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (14)
  10. Dong C. Z., Celik O., Catbas F. N., O'Brien E. J., & Taylor S. (2020). Structural displacement monitoring using deep learning‐based full field optical flow methods. Structure and Infrastructure Engineering, 16(1), 5171.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (15)
  11. Garrido‐Jurado S., Muñoz‐Salinas R., Madrid‐Cuevas F. J., & Marín‐Jiménez M. J. (2014). Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition, 47(6), 22802292.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (16)Digital Library
  12. Geiger A., Moosmann F., Car Ö., & Schuster B. (2012). Automatic camera and range sensor calibration using a single shot. 2012 IEEE International Conference on Robotics and Automation, St Paul, MN (pp. 39363943).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (18)
  13. Girshick R., Donahue J., Darrell T., & Malik J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH (pp. 580587).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (19)
  14. Jeong J. H., & Jo H. (2022). Real‐time generic target tracking for structural displacement monitoring under environmental uncertainties via deep learning. Structural Control and Health Monitoring, 29(3), e2902.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (20)
  15. Li S., Liang Z., & Guo P. (2023). A FBG pull‐wire vertical displacement sensor for health monitoring of medium‐small span bridges. Measurement, 211, 112613.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (21)
  16. Li Y., Huang J., Jiang S. H., Huang F., & Chang Z. (2017). A web‐based GPS system for displacement monitoring and failure mechanism analysis of reservoir landslide. Scientific Reports, 7(1), 113.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (22)
  17. Lu B., Bai B., & Zhao X. (2023). Vision‐based structural displacement measurement under ambient‐light changes via deep learning and digital image processing. Measurement: Journal of the International Measurement Confederation, 208, 112480. https://doi.org/10.1016/j.measurement.2023.112480Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (23)
  18. Luo L., & Feng M. Q. (2018). Edge‐enhanced matching for gradient‐based computer vision displacement measurement. Computer‐Aided Civil and Infrastructure Engineering, 33(12), 10191040.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (24)Digital Library
  19. Lv J., Hu Z., Ren G., Zhang C., & Liu Y. (2019). Research on new FBG displacement sensor and its application in Beijing Daxing airport project. Optik, 178, 146155.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (26)
  20. Ma Z., Choi J., & Sohn H. (2022). Real‐time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements. Computer‐Aided Civil and Infrastructure Engineering, 37(6), 688703.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (27)Digital Library
  21. Ma Z., Choi J., & Sohn H. (2023). Three‐dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi‐rate Kalman filter. Engineering Structures, 292, 116535.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (29)
  22. Ngeljaratan L., Moustafa M. A., & Pekcan G. (2021). A compressive sensing method for processing and improving vision‐based target‐tracking signals for structural health monitoring. Computer‐Aided Civil and Infrastructure Engineering, 36(9), 12031223.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (30)Digital Library
  23. Redmon J., Divvala S., Girshick R., & Farhadi A. (2016). You only look once: Unified, real‐time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2016, Las Vegas, NV (pp. 779788).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (32)
  24. Ronneberger O., Fischer P., & Brox T. (2015). U‐Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer‐Assisted Intervention—MICCAI 2015, Munich, Germany (pp. 234241).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (33)
  25. Rublee E., Rabaud V., Konolige K., & Bradski G. (2011). ORB: An efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision, Barcelona, Spain (pp. 25642571).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (34)
  26. Schweitzer H., Bell J. W., & Wu F. (2002). Very fast template matching. Computer Vision—ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark (pp. 358372).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (35)
  27. Shaik S. A., Hoang T., Mechitov K., & Spencer B. F. (2022). Wireless SmartVision system for synchronized displacement monitoring of railroad bridges. Computer‐Aided Civil and Infrastructure Engineering, 37(9), 10701088.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (36)
  28. Shao Y., Li L., Li J., An S., & Hao H. (2021). Computer vision based target‐free 3D vibration displacement measurement of structures. Engineering Structures, 246, 113040. https://doi.org/10.1016/j.engstruct.2021.113040Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (37)Cross Ref
  29. Song Q., Wu J., Wang H., An Y., & Tang G. (2022). Computer vision‐based illumination‐robust and multi‐point simultaneous structural displacement measuring method. Mechanical Systems and Signal Processing, 170, 108822.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (39)
  30. Sun C., Gu D., & Lu X. (2023). Three‐dimensional structural displacement measurement using monocular vision and deep learning based pose estimation. Mechanical Systems and Signal Processing, 190, 110141.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (40)Cross Ref
  31. Sun Q., Zhang L., Ding X. L., Hu J., Li Z. W., & Zhu J. J. (2015). Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sensing of Environment, 156, 4557.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (42)Cross Ref
  32. Tsai Z. X., You G. J. Y., Lee H. Y., & Chiu Y. J. (2012). Use of a total station to monitor post‐failure sediment yields in landslide sites of the Shihmen reservoir watershed, Taiwan. Geomorphology, 139‐140, 438451.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (44)
  33. Wang J., Gao J., Liu C., & Wang J. (2010). High precision slope deformation monitoring model based on the GPS/Pseudolites technology in open‐pit mine. Mining Science and Technology, 20(1), 126132.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (45)
  34. Wang M., Ao W. K., Bownjohn J., & Xu F. (2022). A novel gradient‐based matching via voting technique for vision‐based structural displacement measurement. Mechanical Systems and Signal Processing, 171, 108951.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (46)
  35. Wu H., Guo Y., Xiong L., Liu W., Li G., & Zhou X. (2019). Optical fiber‐based sensing, measuring, and implementation methods for slope deformation monitoring: A review. IEEE Sensors Journal, 19(8), 27862800.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (47)
  36. Wu T., Tang L., Shao S., Zhang X., Liu Y., Zhou Z., & Qi X. (2022). Accurate structural displacement monitoring by data fusion of a consumer‐grade camera and accelerometers. Engineering Structures, 262, 114303.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (48)
  37. Xu H., Zhang J., Cai J., Rezatofighi H., & Tao D. (2022). GMFlow: Learning optical flow via global matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA (pp. 81218130).Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (49)
  38. Yin Y., Yu Q., Hu B., Zhang Y., Chen W., Liu X., & Ding X. (2023). A vision monitoring system for multipoint deflection of large‐span bridge based on camera networking. Computer‐Aided Civil and Infrastructure Engineering, 38(13), 18791891.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (50)Digital Library
  39. Yu S., Zhang J., & He X. (2020). An advanced vision‐based deformation measurement method and application on a long‐span cable‐stayed bridge. Measurement Science and Technology, 31(6), 065201.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (52)
  40. Zhang Z., & Member S. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 13301334.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (53)Digital Library
  41. Zhao S., Kang F., & Li J. (2018). Displacement monitoring for slope stability evaluation based on binocular vision systems. Optik, 171, 658671.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (55)
  42. Zhu J., Lu Z., & Zhang C. (2021). A marker‐free method for structural dynamic displacement measurement based on optical flow. Structure and Infrastructure Engineering, 18(1), 8496.Google ScholarA multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (56)

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      A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision (58)

      Computer-Aided Civil and Infrastructure Engineering Volume 39, Issue 13

      1 July 2024

      163 pages

      ISSN:1093-9687

      EISSN:1467-8667

      DOI:10.1111/mice.v39.13

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      © 2024 The Authors. Computer‐Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor.

      This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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          • Published: 23 February 2024

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