Paper
21 December 2023 Research on ultrasonic TOFD scanning image weld defect detection method based on PyTorch
Yu Chen, Hailin Chang, Jingman Lai, Shenfeng Guan, Jinsan Ju
Author Affiliations +
Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129702V (2023) https://doi.org/10.1117/12.3012200
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China
Abstract
In order to overcome the shortcomings of low efficiency and subjective factors in manually analyzing and evaluating weld defect images obtained by the ultrasonic TOFD method. In this study, combined with artificial intelligence image recognition technology, an automatic detection algorithm of ultrasonic TOFD scanning image weld defects based on Yolov6 of PyTorch platform was developed. Through continuous optimization of parameter configuration, a recognition model with good recognition effect is obtained. At this time, the recognition and classification of five common defects, namely stoma, slag inclusion, incomplete penetration, lack of fusion, and crackle, are correct on the test set. The correct recognition confidence can reach above 0.9, and the positioning is accurate.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yu Chen, Hailin Chang, Jingman Lai, Shenfeng Guan, and Jinsan Ju "Research on ultrasonic TOFD scanning image weld defect detection method based on PyTorch", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129702V (21 December 2023); https://doi.org/10.1117/12.3012200
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KEYWORDS
Ultrasonics

Machine learning

Detection and tracking algorithms

Image processing

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