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Although training data is essential for machine learning, railway companiesare facing difficulties in gathering adequate images of defective equipment dueto their proactive replacement of would be defective equipment. Nevertheless,proactive replacement is indispensable for safe and undisturbed operation ofpublic transport. In this research, we have developed a model using CycleGAN togenerate artificial images of defective equipment instead of real images. Byadopting these generated images as training data, we verified that these imagesare indistinguishable from real images and they play a vital role in enhancingthe accuracy of the defect detection models.

Railway Anomaly detection model using synthetic defect images generated by CycleGAN