Deep Multitask Learning for Railway Track Inspection

Railroad tracks need to be periodically inspected and monitored to ensure safe transportation. Automated track inspection using computer vision and pattern recognition methods has recently shown the potential to improve safety by allowing for more frequent inspections while reducing human errors. Ac...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2017-01, Vol.18 (1), p.153-164
Main Authors: Gibert, Xavier, Patel, Vishal M., Chellappa, Rama
Format: Article
Language:English
Subjects:
Computer vision
Deep convolutional neural networks
Detectors
Fasteners
Inspection
Machine learning
material identification
multitask learning
Multitasking
Neural networks
Rail transportation
Rails
railway track inspection
Railway tracks
Training
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Summary:Railroad tracks need to be periodically inspected and monitored to ensure safe transportation. Automated track inspection using computer vision and pattern recognition methods has recently shown the potential to improve safety by allowing for more frequent inspections while reducing human errors. Achieving full automation is still very challenging due to the number of different possible failure modes, as well as the broad range of image variations that can potentially trigger false alarms. In addition, the number of defective components is very small, so not many training examples are available for the machine to learn a robust anomaly detector. In this paper, we show that detection performance can be improved by combining multiple detectors within a multitask learning framework. We show that this approach results in improved accuracy for detecting defects on railway ties and fasteners.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2568758