Abstract:
Today, railway transportation is one of the transport
modes commonly used. Compared to other transport modes,
railway traffic is highly critical. Multiple railway vehicles run
constantly on one or two lines. Rail switch passages are used to
prevent locomotives from colliding with one another and avoid
traffic disruptions. Through switch passages, locomotives pass
from one line to another. Friction between rail and wheels on
switch passages is considerably high. This friction leads to
failures on switch passages. Unless these failures are diagnosed
early and remedied, significant accidents emerge.
In this study, a new approach based on image processing has
been presented for detection of rail switch passages on railway
lines. A test vehicle has been created in order to test the proposed
approach and apply it on a real-time system. Railway line is
monitored by digital cameras fixed on this test vehicle. Imageprocessing
approach is developed on the real-time images
captured from the railway line and the switch passages on the
line are detected. The image-processing approach consists of
three main parts including pre-processing, feature extraction and
processing of the features obtained. At the pre-processing stage,
the basic image processing methods are used. At the feature
extraction stage, Canny edge extraction algorithm is used and
hence the edges in the image are detected. Hough transform
method is used at the stage of processing of the extracted
features. Following Hough transform stage, straight lines and
angles of these lines are obtained on the image. Taking into
account the angle of each straight line, the junction points of the
lines are calculated. Thus, rail switch passage and switch types
are detected. The proposed image-processing approach is highly
fast and real time-based. Compared to the existing studies in the
literature, it is seen that the proposed method gives fast and
successful results. This study intends to diagnose the failures on
switch passages early and prevent potential accidents.
