An Approach for Utility Pole Recognition in Real Conditions

  • Alejandro Israel Barranco-Gutiérrez
  • Saúl Martínez-Díaz
  • José Luis Gómez-Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8334)


In this work, we propose an approach for utility pole recognition in real conditions based on color, shape and photometric stereo vision, by using conventional low cost cameras. This subsystem is part of an automatic path planning system for a maintenance robot, which repairs the cable connections in electrical poles. This method could be used in applications requiring object recognition in outdoor environments. The challenges facing this approach include extreme solar illumination, the confusion between telephone poles, cable TV, in columns of buildings, trees, street lights, and tilt between the groundand the pole. The experiments of this recognition system shows satisfactory results under different solar illuminations, different distances between the post and the cameras, different inclinations of pole with respect to the ground, occlusions of the pole and location of the utility pole from cameras system. Results were totally satisfactory with 100% effectiveness in a range of 5% to 95% with respect to the H component of the HSV scheme. The proposed method recognizes and locates utility poles with respect to the stereo vision system.


Color segmentation Stereo vision Utility pole Object shape Solar lighting 


  1. 1.
    He, Y., Tatsuno, K.: An Example of Open Robot Controller Architecture - For Power Distribution Line Maintenance Robot System. World Academy of Science, Engineering and Technology 29, 266–271 (2008)Google Scholar
  2. 2.
    Nakajima, C.: Automatic recognition of facility drawings and street maps utilizing the facility management database. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 516–519 (1995)Google Scholar
  3. 3.
    Cetin, B.: Automated electric utility pole detection from aerial images. In: SOUTHEASTCON 2009, pp. 44–49. IEEE (2009)Google Scholar
  4. 4.
    Igor, K., Amit, A., Ehud, R.: Color Invariants for Person Reidenti- fication. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(7), 1622–1634 (2013)CrossRefGoogle Scholar
  5. 5.
    Berwick, D., Lee, S.: A Chromaticity Space for Specularity, Illumination Color and Illumination Pose-Invariant 3-D Object Recognition. In: Proc. IEEE Intl Conf. Computer Vision, pp. 165–170 (1998)Google Scholar
  6. 6.
    Halawani, S.M., Sunar, M.S.: Interaction between Sunlight and the Sky Colour with 3D Objects in the Outdoor Virtual Environment. In: Proceedings of the 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, AMS 2010, pp. 470–475 (2010)Google Scholar
  7. 7.
    Batllea, J., Casalsb, A., Freixeneta, J., Martı, J.: A review on strategies for recognizing natural objects in colour images of outdoor scenes. Image and Vision Computing 18, 515–530 (2000)CrossRefGoogle Scholar
  8. 8.
    Yokoyama, H., Date, H., Kanai, S., Takeda, H.: Pole-like objects recognition from mobile laser scanning data using smoothing and principal component analysis. In: ISPRS Workshop, Laser scanning 2011, ISPRS, vol. XXXVIII, pp. 115–121 (2011)Google Scholar
  9. 9.
    Peyton, Z., Peebles Jr.: Probability, Random Variables, and Random Signal Principles, pp.77-84. McGraw Hill (2000)Google Scholar
  10. 10.
    Prewitt, J.M.S.: Object Enhancement and Extraction in Picture processing and Psychopictorics. Academic Press (1970)Google Scholar
  11. 11.
    Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Trans. Info. Theory IT-8, 179–187 (1962)Google Scholar
  12. 12.
    Lowe, D.G.: Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence 31(3), 355–395 (1987)CrossRefGoogle Scholar
  13. 13.
    Barranco, A.I., Medel, J.: Automatic object recognition based on dimensional relation. Computación y Sistemas Journal 15(2), 267–272 (2011)Google Scholar
  14. 14.
    Barranco, A.I., Medel, J.: Artificial vision and identification for intelligent orientation using a compass. Revista Facultad de Ingeniería Universidad de Antioquia, Rev. Fac. Ing. Univ. Antioquia N 58, 191–198 (2011)Google Scholar
  15. 15.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  16. 16.
    Rodríguez, G.J., Gómez, J.L., Barranco, A.I., Martínez, S., Sandoval, J.: Visual 3D object recognition and location for manipulator robot. In: Proceedings of CIRC 2013, pp. 217–222 (2013)Google Scholar
  17. 17.
    Burger, W., Burge, M.J.: Digital Image Processing: An Algorithmic Introduction Using Java, pp. 260–265. Springer (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alejandro Israel Barranco-Gutiérrez
    • 1
  • Saúl Martínez-Díaz
    • 1
  • José Luis Gómez-Torres
    • 1
  1. 1.Instituto Tecnológico de La PazMéxico

Personalised recommendations