Learning Based Experimental Approach For Condition Monitoring Using Laser Cameras In Railway Tracks

Year 2016, , 1 - 5, 01.12.2016

Yunus Santur , Mehmet Karaköse , Erhan Akın

https://doi.org/10.18100/ijamec.270656

Cited By: 12

Abstract

Detecting the rail surface faults is one of
the most important components of railway inspection process which should be
performed periodically. Today, the railway inspection process is commonly
performed using computer vision. Performing railway inspection based on image
processing can lead to false-positive results. The fact that the oil and dust
residues occurring on railway surfaces can be detected as an error by the image
processing software can lead to loss of time and additional costs in the
railway maintenance process. In this study, a hardware and software
architecture are presented to perform railway surface inspection using 3D laser
cameras. The use of 3D laser cameras in railway inspection process provides
high accuracy rates in real time. The reading rate of laser cameras to read up
to 25.000 profiles per second is another important advantage provided in real
time railway inspection.  Consequently, a computer vision-based
approach in which 3D laser cameras that could allow for contactless and fast
detection of the railway surface and lateral defects such as fracture, scouring
and wear with high accuracy are used in the railway inspection process was
proposed in the study. 

Keywords

Railway Inspection, Anomaly Detect, Computer Vision, Laser Camera

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