  The external visual inspections of rolling stock's underfloor equipment are
currently being performed via human visual inspection. In this study, we
attempt to partly automate visual inspection by investigating anomaly
inspection algorithms that use image processing technology. As the railroad
maintenance studies tend to have little anomaly data, unsupervised learning
methods are usually preferred for anomaly detection; however, training cost and
accuracy is still a challenge. Additionally, a researcher created anomalous
images from normal images by adding noise, etc., but the anomalous targeted in
this study is the rotation of piping cocks that was difficult to create using
noise. Therefore, in this study, we propose a new method that uses style
conversion via generative adversarial networks on three-dimensional computer
graphics and imitates anomaly images to apply anomaly detection based on
supervised learning. The geometry-consistent style conversion model was used to
convert the image, and because of this the color and texture of the image were
successfully made to imitate the real image while maintaining the anomalous
shape. Using the generated anomaly images as supervised data, the anomaly
detection model can be easily trained without complex adjustments and
successfully detects anomalies.
