Ice Detection on Electrical Power Cables

  • Binglin Li
  • Gabriel ThomasEmail author
  • Dexter Williams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


In northern countries, ice storms can cause major power disruptions such as the one that occurred on December 2013 that left more than 300,000 customers in Toronto with no electricity immediately after such an ice storm. Detection of ice formation on power cables can help on taking actions for removing the ice before a major problem occurs. A computer vision solution was developed to detect ice on difficult imaging scenarios such as images taken under fog conditions that reduces the image contrast, passing cars that are within the field of view of the camera as well as different illumination problems that can occur when taking images during different times of the day. Based on a neural network for classification and six image features that can deal with these difficult images, we reduced the errors on a set of images that was previously yielding 20 errors out of 50 images to only one error.


Power lines Ice detection Neural networks Co-occurrence matrix Hough transform 


  1. 1.
    Tens of thousands in U.S., Canada without power days after ice storm.
  2. 2.
    U.S.-Canada Power System Outage Task Force, Final Report on the August 14, 2003 Blackout in the United States and Canada: Causes and Recommendations, April 2004Google Scholar
  3. 3.
    Zarnani, A., Musilek, P., Shi, X., Ke, X., He, H., Greiner, R.: Learning to predict ice accretion on electric power lines. Eng. Appl. Artif. Intell. 25(3), 609–617 (2012)CrossRefGoogle Scholar
  4. 4.
    Wachal, R., Stoezel, J.S., Peckover, M., Godkin, D.: A computer vision early-warning ice detection system for the smart grid. In: Transmission and Distribution Conference and Exposition (T&D), IEEE PES, pp. 1–6, May 2012Google Scholar
  5. 5.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986)CrossRefGoogle Scholar
  6. 6.
    Sharifi, M., Fathy, M., Mahmoudi, M.T.: A classified and comparative study of edge detection algorithms. In: International Conference on Information Technology: Coding and Computing, pp. 117–120, April 2002Google Scholar
  7. 7.
    Shrivakshan, G.T., Chandrasekar, C.: A comparison of various edge detection techniques used in image processing. Int. J. Comput. Sci. Issues 9(5), 272–276 (2012)Google Scholar
  8. 8.
    Ramamurthy, B., Chandran, K.R.: Content based image retrieval for medical images using canny edge detection algorithm. Int. J. Comput. Appl. 17(6), 0975–8887 (2011)Google Scholar
  9. 9.
    Cheng, H.Y., Weng, C.C., Chen, Y.Y.: Vehicle detection in aerial surveillance using dynamic bayesian networks. IEEE Trans. Image Process. 21(4), 2152–2159 (2012)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Partio, M., Cramariuc, B., Gabbouj, M., Visa, A.: Rock texture retrieval using gray level co-occurrence matrix. In: Proceedings of the 5th Nordic Signal Processing Symposium, vol. 75, October 2002Google Scholar
  11. 11.
    Mohanaiah, P., Sathyanarayana, P., GuruKumar, L.: Image texture feature extraction using GLCM approach. Int. J. Sci. Res. Publ. 3(5) (2013)Google Scholar
  12. 12.
    de Siqueira, F.R., Schwartz, W.R., Pedrini, H.: Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing 120, 336–345 (2013)CrossRefGoogle Scholar
  13. 13.
    Tripathi, N., Panda, S.P.: A review on textural features based computer aided diagnostic system for mammogram classification using GLCM & RBFNN. Int. J. Eng. Trends Technol. 17(9), 462–464 (2014)CrossRefGoogle Scholar
  14. 14.
    Beura, S., Majhi, B., Dash, R.: Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154, 1–14 (2015)CrossRefGoogle Scholar
  15. 15.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man. Cybern. SMC-3(6), 610–621 (1973)CrossRefGoogle Scholar
  16. 16.
    Weszka, J.S., Dyer, C.R., Rosenfeld, A.: A comparative study of texture measures for terrain classification. IEEE Trans. Syst. Man. Cybern. SMC-6(4), 269–285 (1976)CrossRefGoogle Scholar
  17. 17.
    Coburn, C.A., Roberts, A.C.B.: A multiscale texture analysis procedure for improved forest stand classification. Int. J. Remote Sens. 25(20), 4287–4308 (2004)CrossRefGoogle Scholar
  18. 18.
    Barata, C., Ruela, M., Francisco, M., Mendonça, T., Marques, J.S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 8(3), 965–979 (2014)CrossRefGoogle Scholar
  19. 19.
    Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Electrical and Computer Engineering DepartmentUniversity of ManitobaWinnipegCanada
  2. 2.Manitoba HVDC CentreWinnipegCanada

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