Deep Multitask Learning for Railway Track Inspection

• Published in: IEEE transactions on intelligent transportation systems Vol. 18; no. 1; pp. 153 - 164
• Main Authors: Gibert, Xavier, Patel, Vishal M., Chellappa, Rama
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2016.2568758

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.