Skip to main content
SpringerLink
Log in

Freight train gauge-exceeding detection based on three-dimensional stereo vision measurement

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

We present a method for freight train gauge-exceeding detection based on three-dimensional (3D) stereo vision measurement. To reach high measurement accuracy under large-scale situation, the factors which influence the 3D measurement error are analyzed in detail. Algorithm to accurately extract the laser stripe feature projected by the measurement system is described. With the obtained stripe features, the 3D structure of the freight train can be reconstructed with nonlinear optimization procedure. Specially designed targets are used to identify the global coordinate system for gauge-exceeding detection. A prototype has been developed and the reliability and accuracy have been demonstrated by external field experiment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Hermann, G.: Design considerations for a modular high precision 3D coordinate measuring machine. In: International Conference on Mechatronics, pp. 161–165 (2006)

  2. Liu, Z., Xu, Y., Liu, Z., et al.: A large scale 3D positioning method based on a network of rotating laser automatic theodolites. In: International Conference on Information and Automation, pp. 513–518 (2010)

  3. Bouvet, D., Garcia, G., Gorham, B.J., et al.: Precise 3-D localization by automatic laser theodolite and odometer for civil-engineering machines. In: International Conference on Robotics and Automation, pp. 2045–2050 (2001)

  4. Zhan, Z., Zhang, Z., Zhang, J.: An integrated photogrammetric system with metric digital camera and total station. In: International Multi-Symposiums on Computer and Computational Sciences, pp. 747–752 (2006)

  5. Gao, G., Wang, W., Lin, K., et al.: Kinematic calibration for articulated arm coordinate measuring machines base on particle swarm optimization. In: International Conference on Intelligent Computation Technology and Automation, pp. 189–192 (2009)

  6. Gao, G., Wang, W., Lin, K., et al.: Structural parameter identification for articulated arm coordinate measuring machines. In: International Conference on Intelligent Computation Technology and Automation, pp. 128–131 (2009)

  7. Hori, T., Nishida, Y.: Improvement of position estimation of the ultrasonic 3D tag system. In: International Symposium on Robot and Human Interactive Communication, pp. 436–441 (2008)

  8. Massa J.S., Buller G.S., Walker A.C. et al.: Time of flight optical ranging system based on time correlated single photon counting. Appl. Opt. 37(31), 7298–7304 (1998)

    Article  Google Scholar 

  9. Nilsson B., Carlsson T.E.: Direct three dimensional shape measurement by digital light-in-light holography. Appl. Opt. 37(34), 7954–7959 (1998)

    Article  Google Scholar 

  10. Hu Y., Xi J., Li E., Chicharo J. et al.: Three-dimensional profilometry based on shift estimation of projected fringe patterns. Appl. Opt. 45(4), 678–687 (2006)

    Article  Google Scholar 

  11. Baumbach T., Osten W., Kopylow C. et al.: Remote metrology by comparative digital holography. Appl. Opt. 45(5), 925–934 (2006)

    Article  Google Scholar 

  12. Purcell D., Davies A., Farahi F.: Effective wavelength calibration for moiré fringe projection. Appl. Opt. 45(34), 8629–8635 (2006)

    Article  Google Scholar 

  13. Da F., Gai S.: Flexible three-dimensional measurement technique based on a digital light processing projector. Appl. Opt. 47(3), 377–385 (2008)

    Article  Google Scholar 

  14. Wang Y., Lau D.L., Hassebrook L.G.: Fit-sphere unwrapping and performance analysis of 3D fingerprints. Appl. Opt. 49(4), 592–600 (2010)

    Article  Google Scholar 

  15. Wang Z., Du H., Park S. et al.: Three-dimensional shape measurement with a fast and accurate approach. Appl. Opt. 48(6), 1052–1061 (2009)

    Article  Google Scholar 

  16. Wen, G., Zhang, Y., Zong, Y.: 3-D shape measurement based computer vision. In: International Conference on Machine Learning and Cybernetics, pp. 910–914 (2002)

  17. Kurazume, R., Tobata, Y., Iwashita, Y., et al.: 3D laser measurement system for larger scale architectures using multiple mobile robots. In: International Conference on 3-D Digital Imaging and Modeling, pp. 91–98 (2007)

  18. Wang W., Hu Z., Shun Z.: Freight train gauge-exceeding detecting system based on image method. Laser J. 31, 7–9 (2010)

    Google Scholar 

  19. Wang T., Xiao S., Zhu W.: A difference algorithm on image segmentation of gauge monitoring. Comput. Eng. Appl. 11, 212–215 (2004)

    Google Scholar 

  20. Wang T., Xiao S., Zhu W.: An algorithm on image segmentation of moving objects using background information. Comput. Eng. Appl. 12, 34–36 (2004)

    Google Scholar 

  21. Lei B., He S., Mao Q.: Bottom gauge-exceeding measuring system of railway freight cars. Instrum. Tech. Sens. 5, 52–54 (2005)

    Google Scholar 

  22. Sun L., Xiao S.: Algorithm research on freight train gauge inspection system based on structure lighting theorem. Comput. Appl. 25, 213–217 (2005)

    Google Scholar 

  23. Zhang S., Huang P.S.: Novel method for structured light system calibration. Opt. Eng. 45(8), 083601 (2006)

    Article  Google Scholar 

  24. Zhang G., Liu Z., Sun J. et al.: Novel calibration method for a multi-sensor visual measurement system based on structured light. Opt. Eng. 49(4), 043602 (2010)

    Article  MathSciNet  Google Scholar 

  25. Ren Z., Cai L.: Three-dimensional structure measurement of diamond crowns based on stereo vision. Appl. Opt. 48(31), 5917–5932 (2009)

    Article  Google Scholar 

  26. Kim W.S., Ansar A.I., Steele R.D. et al.: Performance analysis and validation of a stereo vision system. IEEE Int. Conf. Syst. Man Cybern. 2, 1409–1416 (2005)

    Google Scholar 

  27. Chang C.C., Chatterjee S., Kube P.R.: A quantization error analysis for convergent stereo. IEEE Int. Conf. Image Process. 2, 735–739 (1994)

    Google Scholar 

  28. Zhang X., Liu Z., Wang S., Zhang Y.: Research on train gauge-exceeding detection system based on dual-processor architecture. Chin. J. Sci. Instrum. 33(1), 91–96 (2012)

    Google Scholar 

  29. Chen F., Brown G.M., Song M.M.: Overview of three-dimensional shape measurement using optical methods. Opt. Eng. 39(1), 10–22 (2000)

    Article  Google Scholar 

  30. Tsar R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV camera and lenses. IEEE J. Robot. Autom. 3(4), 323–344 (1987)

    Article  Google Scholar 

  31. Zhang, Z.: A flexible new technique for camera calibration. In: Seventh International Conference on Computer Vision, pp. 666–673 (1999)

  32. Weng J., Cohen P., Herniou M.: Camera calibration with distortion models and accuracy evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992)

    Article  Google Scholar 

  33. He J., Zhang G.: Study on method for processing image of strip in structure-light 3D vision measuring technique. J. Univ. Aeronaut. Astronaut. 29(7), 93–597 (2003)

    Google Scholar 

  34. Hu B., Li D., Jin G., Hu H.: New method for obtaining the center of structured light stripe by direction template. Comput. Eng. Appl. 11, 59–60 (2002)

    Google Scholar 

  35. Steger C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998)

    Article  MathSciNet  Google Scholar 

  36. Zhou F., Wang F., Zhang G.: Three-step extraction method for line-stripe in structured light vision. Chin. J. Mech. Eng. 44(11), 215–219 (2008)

    Article  MathSciNet  Google Scholar 

  37. Hu K., Zhou F., Zhang G.: Fast extrication method for sub-pixel center of structured light stripe. Chin. J. Sci. Instrum. 27(10), 1326–1329 (2006)

    Google Scholar 

  38. Kovesi, P.: Symmetry and asymmetry from local phase. In: Tenth Australian Joint Conference on Artificial Intelligence, pp. 185–190 (1997)

  39. Kovesi, P.: Phase congruency detects corners and edges. In: The Australian Pattern Recognition Society Conference, pp. 309–318 (2003)

  40. Field D.J.: Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. 4(12), 2379–2394 (1987)

    Article  Google Scholar 

  41. Zhang Z., Deriche R., Faugeras O. et al.: A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artif. Intell. 78, 87–119 (1995)

    Article  Google Scholar 

  42. Zhang Z.: Determining the epipolar geometry and its uncertainty: a review. Int. J. Comput. 27, 161–195 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Optic Communication Engineering, Nanjing University, Nanjing, 210093, China

    Yixin Zhang, Shun Wang, Xuping Zhang, Fei Xie & Jiaqi Wang

Authors
  1. Yixin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fei Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiaqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yixin Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, Y., Wang, S., Zhang, X. et al. Freight train gauge-exceeding detection based on three-dimensional stereo vision measurement. Machine Vision and Applications 24, 461–475 (2013). https://doi.org/10.1007/s00138-012-0444-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-012-0444-2

Keywords

Search

Navigation