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Maintaining high standards for user safety during daily railway operations iscrucial for railway managers. To aid in this endeavor, top- or side-viewcameras and GPS positioning systems have facilitated progress toward automatingperiodic inspections of defective features and assessing the deterioratingstatus of railway components. However, collecting data on deteriorated statuscan be time-consuming and requires repeated data acquisition because of theextreme temporal occurrence imbalance. In supervised learning, thousands ofpaired data sets containing defective raw images and annotated labels arerequired. However, the one-class classification approach offers the advantageof requiring fewer images to optimize parameters for training normal andanomalous features. The deeper fully-convolutional data descriptions (FCDDs)were applicable to several damage data sets of concrete/steel components instructures, 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 damageclassification using the deeper FCDDs for defective railway components. We alsoperformed ablation studies of the deeper backbone based on convolutional neuralnetworks (CNNs). Furthermore, we visualized deterioration features by usingtransposed Gaussian upsampling. We demonstrated our application to railwayinspection using a video acquisition dataset of railway track from backwardview at a cloudy and sunny scene. Finally, we examined the usability of ourapproach for prognostics and future work on railway inspection.

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