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Article

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing

by
Christopher Thembinkosi Mcineka
1,
Nelendran Pillay
2,*,
Kevin Moorgas
2 and
Shaveen Maharaj
2
1
Transnet, 121 Jan Moolman Street, Vryheid 3100, South Africa
2
Department of Electronic and Computer Engineering, Durban University of Technology, Steve Biko Campus, Durban 4001, South Africa
*
Author to whom correspondence should be addressed.
J. Imaging 2024, 10(6), 142; https://doi.org/10.3390/jimaging10060142
Submission received: 7 May 2024 / Revised: 4 June 2024 / Accepted: 6 June 2024 / Published: 11 June 2024

Abstract

This article presents a computer vision-based approach to switching electric locomotive power supplies as the vehicle approaches a railway neutral section. Neutral sections are defined as a phase break in which the objective is to separate two single-phase traction supplies on an overhead railway supply line. This separation prevents flashovers due to high voltages caused by the locomotives shorting both electrical phases. The typical system of switching traction supplies automatically employs the use of electro-mechanical relays and induction magnets. In this paper, an image classification approach is proposed to replace the conventional electro-mechanical system with two unique visual markers that represent the ‘Open’ and ‘Close’ signals to initiate the transition. When the computer vision model detects either marker, the vacuum circuit breakers inside the electrical locomotive will be triggered to their respective positions depending on the identified image. A Histogram of Oriented Gradient technique was implemented for feature extraction during the training phase and a Linear Support Vector Machine algorithm was trained for the target image classification. For the task of image segmentation, the Circular Hough Transform shape detection algorithm was employed to locate the markers in the captured images and provided cartesian plane coordinates for segmenting the Object of Interest. A signal marker classification accuracy of 94% with 75 objects per second was achieved using a Linear Support Vector Machine during the experimental testing phase.
Keywords: computer vision; neutral section; image processing; circular Hough transform; histogram of oriented gradient; classifier computer vision; neutral section; image processing; circular Hough transform; histogram of oriented gradient; classifier

Share and Cite

MDPI and ACS Style

Mcineka, C.T.; Pillay, N.; Moorgas, K.; Maharaj, S. Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing. J. Imaging 2024, 10, 142. https://doi.org/10.3390/jimaging10060142

AMA Style

Mcineka CT, Pillay N, Moorgas K, Maharaj S. Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing. Journal of Imaging. 2024; 10(6):142. https://doi.org/10.3390/jimaging10060142

Chicago/Turabian Style

Mcineka, Christopher Thembinkosi, Nelendran Pillay, Kevin Moorgas, and Shaveen Maharaj. 2024. "Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing" Journal of Imaging 10, no. 6: 142. https://doi.org/10.3390/jimaging10060142

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