2023/05/30 by Miya Nakajima, Nakajima, Miya, Takahiro Saitoh +3
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Flow Measurement and Analysis #Image and Object Detection Techniques #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Non-Destructive Testing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.18614
openalex publication_date 2023/05/30 · openalex created_date 2023/06/01 · openalex updated_date 2026/07/28
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.