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On the Application of the Photogrammetric Method to the Diagnostics of Transport Infrastructure Objects

  • Pavel Elugachev
  • Boris ShumilovEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 260)

Abstract

The implementation of plans to create “smart cities” as one of the most important parts of the digital economy requires the priority development of transport infrastructure, ensuring the movement of people and goods within the city and surrounding areas. The safe operation and maximum throughput of this cyber-physical system are possible provided that a diagnostic technology is created for transport infrastructure objects, including those based on a video recording of road conditions. The algorithm of technical vision, which is proposed to be implemented as a program on mobile devices, for recognizing objects of the transport infrastructure and their defects using stereometry is investigated. The obtained data can be used when planning road repairs, in the analysis of road accidents, to process applications of road users, etc.

Keywords

Roads as a cyber-physical system Photogrammetry Calibration 

References

  1. 1.
    Kupriyanovsky, V., Namiot, D., Sinyagov, S.: Cyber-physical systems as a base for digital economy. Int. J. Open Inf. Technol. 4(2) (2016). Homepage, http://injoit.org/index.php/j1/article/view/266/211. Last accessed 25 Mar 2019
  2. 2.
    Urfi, S.K., Amir, A., Khalil, S., Hoda, M.F.: Risk factors for road traffic accidents with head injury in Aligarh. Int. J. Med. Sci. Pub. Health 5, 2103–2107 (2016)CrossRefGoogle Scholar
  3. 3.
    Abdi, T., Hailu, B., Andualem, Adal T., Gelder, P.H.A.J.M., Hagenzieker, M., Carbon, C.-C.: Road crashes in Addis Ababa, ethiopia: empirical findings between the years 2010 and 2014. AFRREV 11, 1–13 (2017)Google Scholar
  4. 4.
    Global status report on road safety (2018). Homepage, http://www.who.int/violence\_injury\_prevention/road\_safety\_status/2018/en/. Last accessed 25 Mar 2019
  5. 5.
    Morales, A., Sanchez-Aparicio, L.J., Gonzalez-Aguilera, D., Gutierrez, M.A., Lopez, A.I., Hernandez-Lopez, D., Rodriguez-Gonzalvez, P.: A new approach to energy calculation of road accidents against fixed small section elements based on close-range photogrammetry. Rem. Sens. 9(1219), 1–18 (2017)Google Scholar
  6. 6.
    Bao, G.: Road distress analysis using 2D and 3D information. The University of Toledo, The University of Toledo Digital Repository Theses and Dissertations (2010)Google Scholar
  7. 7.
    Tsvetkov V.Y.: Control with the use of cyber-physical systems. Perspect. Sci. Educ. 3(27), 55–60 (2017). Int. Sci. Electron. J. Homepage. http://psejournal.wordpress.com/archive17/17-03/. Last accessed 25 Mar 2019
  8. 8.
    Elugachev, P., Shumilov, B.: Development of the technical vision algorithm. In: MATEC Web of Conferences, vol 216, p. 04003. Polytransport Systems, 7p (2018)Google Scholar
  9. 9.
    Rogers, D.F., Adams, J.A.: Mathematical Elements for Computer Graphics. McGraw-Hill, New York (1990)Google Scholar
  10. 10.
    Tsai, R.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom. 3, 323–344 (1987)CrossRefGoogle Scholar
  11. 11.
    Slama, C. (ed.) Manual of Photogrammetry. American Society of Photogrammetry, Falls Church, VA (1980)Google Scholar
  12. 12.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1052–1067 (2007)CrossRefGoogle Scholar
  13. 13.
    Chiuso, A., Favaro, P., Jin, H., Soatto, S.: Structure from motion causally integrated over time. IEEE Trans. Pattern Anal. Mach. Intell. 24, 523–535 (2002)CrossRefGoogle Scholar
  14. 14.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  15. 15.
    Euler angles. Homepage. https://en.wikipedia.org/wiki/Euler_angles#Rotation_matrix. Last accessed 11 June 2018
  16. 16.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing. Analysis and Machine Vision. Thomson, Toronto (2008)Google Scholar
  17. 17.
    Harris affine region detector. Homepage. https://en.wikipedia.org/wiki/Harris_affine_region_detector. Last accessed 11 June 2018
  18. 18.
    Tuytelaars, T., Mikolajczyk, K.: Foundations and Trends in Computer Graphics and Vision, vol 3, pp. 177–280 (2007)Google Scholar
  19. 19.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Fourth Alvey Vision Conference, pp. 147–151. Manchester, UK (1988)Google Scholar
  20. 20.
    Niblack, W.: An Introduction to Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ, USA (1986)Google Scholar
  21. 21.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. SMC 9(1), 62–66 (1979)Google Scholar
  22. 22.
    Bayer, B.: An optimum method for two-level rendition of continuous tone pictures. In: IEEE International Conference on Communications, vol. 1, pp. 11–15 (1973)Google Scholar
  23. 23.
    Floyd, R.W.: An adaptive algorithm for spatial gray-scale. Proc. Soc. Inf. Disp. 17(2), 75–78 (1976)Google Scholar
  24. 24.
    Wang, Z., Bovik, A.C.: Modern Image Quality Assessment. Morgan and Claypool Publishing Company, New York (2006)CrossRefGoogle Scholar
  25. 25.
    Shumilov, B., Gerasimova, Y., Makarov, A.: On binarization of images at the pavement defects recognition. In: 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), pp. 107–110. Saint Petersburg, Russia (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tomsk State University of Architecture and BuildingTomskRussia

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