It is critical for railway managers to maintain a high standard to ensure
user safety during daily operations. Top-view or side-view cameras and GPS
positioning system have enabled progress toward automating the periodic
inspection of defective features and assessing the deteriorated status of the
railway components. Frequently, collecting deteriorated status data constraints
time consuming and repeated data acquisition, because the temporal occurrence
is extremely imbalanced. Supervised learning approach requires thousands of
paired dataset of defective raw images and annotated labels. However, one-class
classification approach has a merit that fewer images enables us to optimize
the parameters for training normal and anomalous feature. Simultaneously, the
visual heat map explanation enables us to discriminate the localized damage
feature. In this paper, we propose a prognostic discriminator pipeline to
automate one-class damage classification towards defective railway components.
We also sensitivity analyze toward the backbone and the receptive field based
on convolutional neural networks (CNNs) using pretrained networks: baseline
CNN27, VGG16, ResNet101, and Inception Networks. We also visualize the
explanation of the defective railway feature using a transposed Gaussian
upsampling. We demonstrate our application for railway inspection in an
open-accessed dataset of defective railway components, and wood-sleeper
deterioration in rural railway. The heatmap is so important that the
hazard-marks could cause an operational delay, an urgent inspection, and
unexpected accident to passenger impact in railway inspection. Furthermore, we
mention its usability for prognostic monitoring and future works for railway
components inspection in the predictive maintenance of railway systems.

本文提出了一种基于卷积神经网络（CNNs）的预测鉴别器流水线来自动分类损坏的铁路组件，使用一类分类方法，结合基于可视化的热图来判定铁路组件的损坏情况，并在预测性维护中提出了未来工作。