Comparison of thresholding algorithms for automatic overhead line detection procedure

• Autorzy: Kowalski Paweł , Smyka Robert

Identyfikatory

DOI

10.15199/48.2022.03.34

• Języki publikacji: EN

Abstrakty

EN

The article presents an overview of the thresholding algorithms. It compares the algorithms proposed by Pun, Kittler, Niblack, Huang, Rosenfeld, Remesh, Lloyd, Riddler, Otsu, Yanni, Kapur and Jawahar. Additionally, it was tested how the tuning of the Pun, Jawahar and Niblack methods affects the thresholding efficiency and proposed a combination of the Pun algorithm with a priori algorithm. All presented algorithms have been implemented and tested for effectiveness in detecting overhead lines.

PL

W artykule przedstawiono przegląd algorytmów progowania. Porównano w nim algorytmy zaproponowane przez Puna, Kittlera, Niblacka, Huanga, Rosenfelda, Remesha, Lloydaa, Riddlera, Otsu, Yanni, Kapura i Jawahara. Dodatkowo przetestowano, jak dostrajanie metod Puna, Jawahara i Niblacka wpływa na skuteczność progowania oraz zaproponowano połączenie algorytmu Puna z algorytmem a priori. Wszystkie przedstawione algorytmy zostały zaimplementowane i przetestowane pod kątem skuteczności w wykrywaniu linii napowietrznych

Słowa kluczowe

EN

thresholding; power line; image processing

PL

progowanie; linia elektroenergetyczna; przetwarzanie obrazu

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2022
• Tom: R. 98, nr 3
• Strony: 149--155
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Kowalski Paweł

• Department of Electrical and High Voltage Engineering, Faculty of Electrical and Control Engineering, Gdańsk University of Technology, ul. Gabriela Narutowicza 11/12 80- 233 Gdańsk, Poland

autor

Smyka Robert

• Department of Electrical and High Voltage Engineering, Faculty of Electrical and Control Engineering, Gdańsk University of Technology, ul. Gabriela Narutowicza 11/12 80- 233 Gdańsk, Poland

Bibliografia

• [1] W. Dołe ̨ga, “Bezpieczeństwo pracy krajowych sieci dystrybucyjnych,” Przegląd Elektrotechniczny, vol. 96, pp. 21–24, 2020.
• [2] W.Dołęga ,“Awarie krajowych linii napowietrznych– wybrane aspekty,” Przegląd Elektrotechniczny, vol. 97, pp. 9–14, 2021.
• [3] T.Tambi, “Real-time monitoring of high voltage insulators in the tropical climate,” Przegląd Elektrotechniczny, vol. 96, pp. 129– 135, 2020.
• [4] P. Kowalski, Application of image processing methods for over- head power lines monitoring. PhD thesis, Gdansk University of Technology, 2020. (in Polish).
• [5] P. Kowalski, M. Czyżak, and R. Smyk, “Comparison of edge detection algorithms for electric wire recognition,” in ITM Web of Conferences, vol. 19, p. 01044, EDP Sciences, 2018.
• [6] P. Kowalski and M. Czyżak, “High voltage line distance measurement and position detection based on stereoscopic image,” in 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 54–57, May 2018.
• [7] P. Kowalski and R. Smyk, “Overhead wires detection by fpga real-time image processing,” in ITM Web of Conferences, vol. 28, p. 01046, 2019.
• [8] C.E.Shannon, “A mathematical theory of communication,”Bell system technical journal, vol. 27, no. 3, pp. 379–423, 1948.
• [9] W. Niblack, An introduction to digital image processing. Strandberg Publishing Company, 1985.
• [10] T. Ridler and S. Calvard, “Picture thresholding using an iterative selection method,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, pp. 630–632, Aug 1978.
• [11] D. Lloyd, “Automatic target classification using moment invariant of image shapes,” IDN AW126, RAE, Farnborough, Reino Unido, 1985.
• [12] M.Yanniand E.Horne, “A new approach to dynamic thresholding,” in EUSIPCO’94: 9th European Conf. Sig. Process, vol. 1, pp. 34–44, 1994.
• [13] A.Rosenfeld and P. De La Torre,“Histogram concavity analysis as an aid in threshold selection,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-13, pp. 231–235, March 1983.
• [14] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, pp. 62–66, Jan 1979.
• [15] A. Wodołażski, “Wybrane algorytmy uczenia maszynowego w segmentacji obrazu kłaczków osadów ściekowych,” Przegląd Elektrotechniczny, vol. 97, pp. 134–136, 2021.
• [16] L.-K. Huang and M.-J. J. Wang, “Image thresholding by minimizing the measures of fuzziness,” Pattern recognition, vol. 28, no. 1, pp. 41–51, 1995.
• [17] C. Jawahar, P. K. Biswas, and A. Ray, “Investigations on fuzzy thresholding based on fuzzy clustering,” Pattern Recognition, vol. 30, no. 10, pp. 1605–1613, 1997.
• [18] J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern recognition, vol. 19, no. 1, pp. 41–47, 1986.
• [19] H. Yan, “Unified formulation of a class of image thresholding techniques,” Pattern Recognition, vol. 29, no. 12, pp. 2025– 2032, 1996.
• [20] N. Ramesh, J. . Yoo, and I. K. Sethi, “Thresholding based on histogram approximation,” IEE Proceedings - Vision, Image and Signal Processing, vol. 142, pp. 271–279, Oct 1995.
• [21] J. N. Kapur, P. K. Sahoo, and A. K. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Computer vision, graphics, and image processing, vol. 29, no. 3, pp. 273–285, 1985.
• [22] T. Pun, “A new method for grey-level picture thresholding using the entropy of the histogram,” Signal processing, vol. 2, no. 3, pp. 223–237, 1980.
• [23] T. Pun, “Entropic thresholding, a new approach,” Computer graphics and image processing, vol. 16, no. 3, pp. 210–239, 1981.
• [24] I. Sobel and G. Feldman, “An isotropic 3x3 image gradient operator. stanford artificial intelligence project (sail),” 1968.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).