# Deep Multi-task Learning for Railway Track Inspection

arXiv: https://arxiv.org/abs/1509.05267 · HTML: https://mlsanity.com/paper/1509.05267/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2015.

## Built on by (most cited first, 5 of 5)

- [Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework](https://mlsanity.com/paper/2102.01882/) (2021)
- [Automatic Rail Component Detection Based on AttnConv-Net](https://mlsanity.com/paper/2108.02423/) (2021)
- [SCNet: A Generalized Attention-based Model for Crack Fault Segmentation](https://mlsanity.com/paper/2112.01426/) (2021)
- [Intelligent Railroad Grade Crossing: Leveraging Semantic Segmentation and Object Detection for Enhanced Safety](https://mlsanity.com/paper/2403.11060/) (2024)
- [Milestone Determination for Autonomous Railway Operation](https://mlsanity.com/paper/2510.06229/) (2025)

## Abstract (arXiv)

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.