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

TL;DR

This paper presents an unsupervised deep learning approach using FCDDs to detect deterioration in wooden railway sleepers, reducing the need for extensive labeled data and enabling automated railway component inspection.

Contribution

The study introduces a novel pipeline employing deeper FCDDs for one-class damage classification in railway components, including ablation studies and deterioration visualization.

Findings

Effective detection of wooden sleeper deterioration in railway videos

Reduced data annotation requirements for damage detection

Demonstrated applicability in real-world railway inspection scenarios

Abstract

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.