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

## TL;DR

This paper introduces a computer vision system to automatically switch electric locomotive power near railway neutral sections using image processing techniques.

## Contribution

A novel image classification approach is proposed to replace traditional electro-mechanical systems for switching locomotive power.

## Key findings

- A Linear Support Vector Machine achieved 94% accuracy in classifying signal markers.
- The system processes 75 objects per second during experimental testing.
- The Circular Hough Transform was effectively used for marker localization.

## 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.