Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing

• URL: http://arxiv.org/abs/2305.18614v1
• Date: Tue, 30 May 2023 00:19:12 GMT
• Title: Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing
• Authors: Miya Nakajima, Takahiro Saitoh, Tsuyoshi Kato
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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