Railway Tracks Detection of Railways Based On Computer Vision Technique and GNSS Data

Abstract

Railway networks are major components of any country’s infrastructure. In Egypt, the length of the railway network is 9570 km, where, 85% of the lines' movements are operated by mechanical signals. In July 2017, the Egyptian government launched a series of railway infrastructure projects aimed to modernize the Egyptian National Railways which includes the infrastructures rehabilitation for the network. This research focuses on developing an efficient low-cost framework using video camera and computer vision techniques for automatic railway track detection. A computer vision technique is used for automatic detection of railway tracks. Interior orientation parameters are extracted as part of the camera calibration task. Bundle adjustment calibration technique is used to compute the exterior orientation parameters based on selected ground control points. Hence, the eye fish effect of the images is removed and orthogonal images for the railway tracks are constructed using matching and feature extraction algorithms. The framework is tested on a dataset of a railway network with an approximate length of 20 km. The accuracy of the results is compared with a field survey data conducted to the same area using conventional surveying instruments such as Total station and Global Navigation Satellite System (GNSS). The proposed framework enables automatic extraction of railway tracks and its relationship with surrounding features, which contributes to quality control and assurance procedures for field collected data. The framework also offers a method for continuous and low-cost monitoring of the railway network. This will help to rapidly assess maintenance requirements for the network.