  In recent years, laser ultrasonic visualization testing (LUVT) has attracted
much attention because of its ability to efficiently perform non-contact
ultrasonic non-destructive testing.Despite many success reports of deep
learning based image analysis for widespread areas, attempts to apply deep
learning to defect detection in LUVT images face the difficulty of preparing a
large dataset of LUVT images that is too expensive to scale. To compensate for
the scarcity of such training data, we propose a data augmentation method that
generates artificial LUVT images by simulation and applies a style transfer to
simulated LUVT images.The experimental results showed that the effectiveness of
data augmentation based on the style-transformed simulated images improved the
prediction performance of defects, rather than directly using the raw simulated
images for data augmentation.
