A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways

Year 2016, Special Issue (2016), 319 - 325, 01.12.2016

Canan Taştimur , Mehmet Karaköse , Erhan Akın

https://doi.org/10.18100/ijamec.270634

Cited By: 18

Abstract

Rail track, turnout
and level crossing are critical parts of railway components. So faults of rail
surface are directly related to the operation safety. The defects occurring in
switches can lead to accidents and the derailment of the train. Therefore, it is
necessary to utilize advanced technology to monitor rail damage of the switch
and level crossing zones. In this study, the switch and level crossing
detection are performed with vision based contactless image processing
techniques. Edge detection algorithm, image processing filters, morphological
feature extraction, Hough transform and hierarchical SVM classifier are
utilized to detect turnout and level crossing regions in the railway
images.  While switch points are
detected, images of the area in front of the train are continuously taken from
camera placed on the train. These images contain foreign objects such as
buildings, trees, etc. The foreign objects existing in the railway image make
it difficult to detect accurately the rail track. The turnout and level
crossing detection cannot also be performed using the information of the rail track
that cannot be correctly detected.  The
several processes, such as some image processing filters and morphological
feature extraction, have been performed to solve this problem. With destroying
the foreign objects from the railway images, both the accuracy rate of the
proposed method has been increased and real-time implementation of the method
is provided.  The Hough transform is
utilized to detect rail track, switch and level crossing in the proposed
method. The turnout detection is performed by applying SVM classifier to
railway image. The proposed method provides real-time rail track, level
crossing and turnout detection. Furthermore, this method has gained dynamic
feature due to not be utilized any template image for switch detection. When
evaluated from these aspects, both the proposed method is superior to other
methods in literature and results were obtained in a shorter time thanks to
applying ROI Segmentation to the railway image.

Keywords

Railroad switch detection, Level crossing detection, Hough transform, Image processing filters, SVM classifier, Edge detection algorithm

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