Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing with Style Transfer

Abstract

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 which is prohibitively expensive and time-consuming to scale. To compensate for the scarcity of such training data, we propose a data augmentation method that generates artificial LUVT images by simulating artificial LUVT images and then applying a style transfer to these simulated 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.