# Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs

arXiv: https://arxiv.org/abs/2305.05103 · HTML: https://mlsanity.com/paper/2305.05103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 7 of 7)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [Rethinking Assumptions in Deep Anomaly Detection](https://mlsanity.com/paper/2006.00339/) (2020)
- [One-class Damage Detector Using Deeper Fully-Convolutional Data Descriptions for Civil Application](https://mlsanity.com/paper/2303.01732/) (2023)

## Built on by (most cited first, 1 of 1)

- [Understanding Deterioration Random Effects for Causal Discovery in Infrastructure Management](https://mlsanity.com/paper/2605.20400/) (2026)

## Abstract (arXiv)

Maintaining high standards for user safety during daily railway operations is crucial for railway managers. To aid in this endeavor, top- or side-view cameras and GPS positioning systems have facilitated progress toward automating periodic inspections of defective features and assessing the deteriorating status of railway components. However, collecting data on deteriorated status can be time-consuming and requires repeated data acquisition because of the extreme temporal occurrence imbalance. In supervised learning, thousands of paired data sets containing defective raw images and annotated labels are required. However, the one-class classification approach offers the advantage of requiring fewer images to optimize parameters for training normal and anomalous features. The deeper fully-convolutional data descriptions (FCDDs) were applicable to several damage data sets of concrete/steel components in structures, and fallen tree, and wooden building collapse in disasters. However, it is not yet known to feasible to railway components. In this study, we devised a prognostic discriminator pipeline to automate one-class damage classification using the deeper FCDDs for defective railway components. We also performed ablation studies of the deeper backbone based on convolutional neural networks (CNNs). Furthermore, we visualized deterioration features by using transposed Gaussian upsampling. We demonstrated our application to railway inspection using a video acquisition dataset of railway track from backward view at a cloudy and sunny scene. Finally, we examined the usability of our approach for prognostics and future work on railway inspection.