Abstract
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.
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Tens of thousands in U.S., Canada without power days after ice storm. http://www.cnn.com/2013/12/25/us/winter-weather/
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 2004
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)
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 2012
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986)
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 2002
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)
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)
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)
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 2002
Mohanaiah, P., Sathyanarayana, P., GuruKumar, L.: Image texture feature extraction using GLCM approach. Int. J. Sci. Res. Publ. 3(5) (2013)
de Siqueira, F.R., Schwartz, W.R., Pedrini, H.: Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing 120, 336–345 (2013)
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)
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)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man. Cybern. SMC-3(6), 610–621 (1973)
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)
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)
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)
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)
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Li, B., Thomas, G., Williams, D. (2015). Ice Detection on Electrical Power Cables. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_33
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DOI: https://doi.org/10.1007/978-3-319-27863-6_33
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