Smart Structures and Systems

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Exploration of temperature effect on videogrammetric technique for displacement monitoring

  • Zhou, Hua-Fei (College of Civil Engineering and Architecture, Wenzhou University) ;
  • Lu, Lin-Jun (College of Civil Engineering and Architecture, Wenzhou University) ;
  • Li, Zhao-Yi (College of Civil Engineering and Architecture, Wenzhou University) ;
  • Ni, Yi-Qing (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • Received : 2019.01.27
  • Accepted : 2020.01.10
  • Published : 2020.02.25
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Abstract

There has been a sustained interest towards the non-contact structural displacement measurement by means of videogrammetric technique. On the way forward, one of the major concerns is the spurious image drift induced by temperature variation. This study therefore carries out an investigation into the temperature effect of videogrammetric technique, focusing on the exploration of the mechanism behind the temperature effect and the elimination of the temperature-caused measurement error. 2D videogrammetric measurement tests under monotonic or cyclic temperature variation are first performed. Features of measurement error and the casual relationship between temperature variation and measurement error are then studied. The variation of the temperature of digital camera is identified as the main cause of measurement error. An excellent linear relationship between them is revealed. After that, camera parameters are extracted from the mapping between world coordinates and pixels coordinates of the calibration targets. The coordinates of principle point and focal lengths show variations well correlated with temperature variation. The measurement error is thought to be an outcome mainly attributed to the variation of the coordinates of principle point. An approach for eliminating temperature-caused measurement error is finally proposed. Correlation models between camera parameters and temperature are formulated. Thereby, camera parameters under different temperature conditions can be predicted and the camera projective matrix can be updated accordingly. By reconstructing the world coordinates with the updated camera projective matrix, the temperature-caused measurement error is eliminated. A satisfactory performance has been achieved by the proposed approach in eliminating the temperature-caused measurement error.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Science Technology Department of Zhejiang Province, Wenzhou Science and Technology Bureau, Zhejiang Provincial National Science Foundation of China

References

  1. Abdel-Aziz, Y.I. and Karara, H.M. (2015), "Direct linear transformation from comparator coordinates into object-space coordinates in close-range photogrammetry", Photogrammetric Eng. Remote Sensing, 81, 103-107. https://doi.org/10.14358/PERS.81.2.103
  2. Adamczyk, M., Liberadzki, P. and Sitnik, R. (2018), "Temperature Compensation Method for Digital Cameras in 2D and 3D Measurement Applications", Sensors, 18(11), 3685. https://doi.org/10.3390/s18113685
  3. Baqersad, J., Poozesh, P., Niezrecki, C. and Avitabile, P. (2017), "Photogrammetry and optical methods in structural dynamics -A review", Mech. Syst. Signal Process., 86, 17-34. https://doi.org/10.1016/j.ymssp.2016.02.011
  4. Brownjohn, J.M.W., Xu, Y. and Hester, D. (2017), "Vision-based bridge deformation monitoring", Front. Built Environ., 3, 23. https://doi.org/10.3389/fbuil.2017.00023
  5. Busca, G., Cigafa, A., Mazzoleni, P. and Zappa, E. (2014), "Vibration monitoring of multiple bridge points by means of a unique vision-based measuring system", Experim. Mech., 54, 255-271. https://doi.org/10.1007/s11340-013-9784-8
  6. Chang, C.C. and Xiao, X.H. (2010), "An integrated visual-inertial technique for structural displacement and velocity measurement", Smart Struct. Syst., Int. J., 6(9), 1025-1039. https://doi.org/10.12989/sss.2010.6.9.1025
  7. Chatterjee, C. and Roychowdhury, V.P. (2000), "Algorithms for coplanar camera calibration", Mach. Vision Applicat., 12, 84-97. https://doi.org/10.1007/s001380050127
  8. Cho, S., Lee, J. and Sim, S.H. (2018), "Comparative study on displacement measurement sensors for high-speed railroad bridge", Smart Struct. Syst., Int. J., 21(5), 637-652. https://doi.org/10.12989/sss.2018.21.5.637
  9. Daakir, M., Zhou, Y., Deseilligny, M.P., Thom, C., Martin, O. and Rupnik, E. (2019), "Improvement of photogrammetric accuracy by modeling and correcting the thermal effect on camera calibration", ISPRS J. Photogrammetry Remote Sensing, 148, 142-155. https://doi.org/10.1016/j.isprsjprs.2018.12.012
  10. Faugeras, O.D. and Toscani, G. (1986), "The calibration problem for stereo", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, New York, USA.
  11. Feng, D.M. and Feng, M.Q. (2016), "Vision-based multipoint displacement measurement for structural health monitoring", Struct. Control Health Monitor., 23, 876-890. https://doi.org/10.1002/stc.1819
  12. Feng, D.M. and Feng, M.Q. (2017), "Experimental validation of cost-effective vision-based structural health monitoring", Mech. Syst. Signal Process., 88, 199-211. https://doi.org/10.1016/j.ymssp.2016.11.021
  13. Feng, D.M. and Feng, M.Q. (2018), "Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection - A review", Eng. Struct., 156, 105-117. https://doi.org/10.1016/j.engstruct.2017.11.018
  14. Geiger, A., Moosmann, F., Car, O. and Schuster, B. (2012), "Automatic camera and range sensor calibration using a single shot", Proceedings of 2012 IEEE International Conference on Robotics and Automation, IEEE, New York, USA.
  15. Handel, H. (2009), "Analyzing the influences of camera warm-up effects on image acquisition", IPSJ Transact. Comput. Vision Applicat., 1, 12-20. https://doi.org/10.1007/978-3-540-76390-1_26
  16. Jiang, R.N., Jauregui, D.V. and White, K.R. (2008), "Close-range photogrammetry applications in bridge measurement: Literature review", Measurement, 41, 823-834. https://doi.org/10.1016/j.measurement.2007.12.005
  17. Lee, J.J., Fukuda, Y., Shinozuka, M., Cho, S.J. and Yun, C.B. (2007), "Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures", Smart Struct. Syst., Int. J., 3(3), 373-384. https://doi.org/10.12989/sss.2007.3.3.373
  18. Lee, J.H., Ho, H.N., Shinozuka, M. and Lee, J.J. (2012), "An advanced vision-based system for real-time displacement measurement of high-rise buildings", Smart Mater. Struct., 21, 125019. https://doi.org/10.1088/0964-1726/21/12/125019
  19. Lee, J.H., Jung, C.Y., Choi, E. and Cheung, J.H. (2017), "Visionbased multipoint measurement systems for structural in-plane and out-of-plane movements including twisting rotation", Smart Struct. Syst., Int. J., 20(5), 563-572. https://doi.org/10.12989/sss.2017.20.5.563g
  20. Liu, T.S., Burner, A.W., Jones, T.W. and Barrows, D.A. (2012), "Photogrammetric techniques for aerospace applications", Progress Aerosp. Sci., 54, 1-58. https://doi.org/10.1016/j.paerosci.2012.03.002
  21. Merchant, D.C. (2006), "Influence of Temperature on Focal Length for the Airborne Camera", Proceedings of San Antonio 2006 ASPRS-MAPPS Fall Conference, San Antonio, TX, USA.
  22. Olaszek, P. (1999), "Investigation of the dynamic characteristic of bridge structures using a computer vision method", Measurement, 25, 227-236. https://doi.org/10.1016/S0263-2241(99)00006-8
  23. Podbreznik, P. and Potocnik, B. (2012), "Assessing the influence of temperature variations on the geometrical properties of a low-cost calibrated camera system by using computer vision procedures", Mach. Vision Applicat., 23, 953-966. https://doi.org/10.1007/s00138-011-0330-3
  24. Poulin-Girard, A.S., Dallaire, X., Veillette, A., Thibault, S. and Laurendeau, D. (2014), "Study of camera calibration process with ray tracing", In: Current Developments in Lens Design and Optical Engineering XV, Proceedings of SPIE, (R.B. Johnson, V.N. Mahajan, S. Thibault Eds.), Vol. 9192, SPIE Press, Bellingham, WA, USA.
  25. Robson, S., Clarke, T.A. and Chen, J. (1993), "The suitability of the Pulnix TM6CN CCD camera for photogrammetric measurement", In: Videometrics II, Proceedings of SPIE, (S.F. El-Hakim Ed.), Vol. 2067, SPIE Press, Bellingham, WA, USA.
  26. Salvi, J., Armangue, X. and Batlle, J. (2002), "A comparative review of camera calibrating methods with accuracy evaluation", Pattern Recognit., 35, 1617-1635. https://doi.org/10.1016/S0031-3203(01)00126-1
  27. Sharpe Jr., W.N. (2008), Handbook of experimental solid mechanics, Springer US, New York, USA.
  28. Smith, M.J. and Cope, E. (2010), "The effects of temperature variation on single-lens-reflex digital camera calibration parameters", International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38 (Part 5), 554-559.
  29. Sun, W. and Cooperstock, J.R. (2006), "An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques", Mach. Vision Applicat., 17, 51-67. https://doi.org/10.1007/s00138-006-0014-6
  30. Surhone, L.M., Tennoe, M.T. and Henssonow, S.F. (2010), Videogrammetry, Betascript Publishing.
  31. Tsai, R.Y. (1987), "A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses", IEEE J. Robotics Automat., 3, 323-344. https://doi.org/10.1109/JRA.1987.1087109
  32. Wahbeh, A.M., Caffrey, J.P. and Masri, S.F. (2003), "A visionbased approach for the direct measurement of displacements in vibrating systems", Smart Mater. Struct., 12, 785-794. https://doi.org/10.1088/0964-1726/12/5/016
  33. Weng, J., Cohen, P. and Herniou M. (1992), "Camera calibration with distortion models and accuracy evaluation", IEEE Transact. Pattern Anal. Mach. Intel., 14, 965-980. https://doi.org/10.1109/34.159901
  34. Widenhorn, R., Blouke, M.M., Weber, A., Rest, A. and Bodegom, E. (2002), "Temperature dependence of dark current in a CCD", In: Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications III, Proceedings of SPIE, (N. Sampat, M.M. Blouke, J. Canosa Eds.), Vol. 4669, SPIE Press, Bellingham, WA, USA.
  35. Xu, Y. and Brownjohn, J.M.W. (2018), "Review of machine-vision based methodologies for displacement measurement in civil structures", J. Civil Struct. Health Monitor., 8, 91-110. https://doi.org/10.1007/s13349-017-0261-4
  36. Yu, Q.F., Chao, Z.C., Jiang, G.W., Shang, Y., Fu, S.H., Liu, X.L., Zhu, X.W. and Liu, H.B. (2014), "The effects of temperature variation on videometric measurement and a compensation method", Image Vision Comput., 32, 1021-1029. https://doi.org/10.1016/j.imavis.2014.08.011
  37. Zhang, Z.Y. (2000), "A flexible new technique for camera calibration", IEEE Transact. Pattern Anal. Mach. Intel., 22, 1330-1334. https://doi.org/10.1109/34.888718
  38. Zhou, H.F., Zheng, J.F., Xie, Z.L., Lu, L.J., Ni, Y.Q. and Ko, J.M. (2017), "Temperature effects on vision measurement system in long-term continuous monitoring of displacement", Renew. Energy, 114, 968-983. https://doi.org/10.1016/j.renene.2017.07.104
  39. Zhou, H.F., Lu, L.J., Li, Z.Y. and Ni, Y.Q. (2019), "Performance of videogrammetric displacement monitoring technique under varying ambient temperature", Adv. Struct. Eng., 22(16), 3371-3384. https://doi.org/10.1177/1369433218822089