Deep Multi-task Learning for Railway Track Inspection
This work addresses safety in railway transportation by improving automated inspection, though it is incremental as it builds on existing multi-task learning methods.
The paper tackles the problem of automated railway track inspection by addressing challenges like multiple failure modes, image variations, and limited defective training examples, and shows that a multi-task learning framework combining multiple detectors improves defect detection accuracy on railway ties and fasteners.
Railroad tracks need to be periodically inspected and monitored to ensure safe transportation. Automated track inspection using computer vision and pattern recognition methods have 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. Also, 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 multi-task learning framework. We show that this approach results in better accuracy in detecting defects on railway ties and fasteners.