Simulation-Aided Deep Learning for Laser Ultrasonic Visualization
  Testing
Miya Nakajima Takahiro Saitoh Tsuyoshi Kato
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.