  Although training data is essential for machine learning, railway companies
are facing difficulties in gathering adequate images of defective equipment due
to their proactive replacement of would be defective equipment. Nevertheless,
proactive replacement is indispensable for safe and undisturbed operation of
public transport. In this research, we have developed a model using CycleGAN to
generate artificial images of defective equipment instead of real images. By
adopting these generated images as training data, we verified that these images
are indistinguishable from real images and they play a vital role in enhancing
the accuracy of the defect detection models.
