Study on intelligent and visualization method of ultrasonic testing of composite materials based on deep learning

•The DiMP object tracking model is improved by using Wasserstein distance to realize the visual localization of the ultrasonic probe.•A 1DCNN classification network is designed to classify one-dimensional ultrasonic signals.•A data connection is proposed to make the two models work together.•The pro...

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Vydané v:	Applied acoustics Ročník 207; s. 109363
Hlavní autori:	Hu, Qichun, Wei, Xiaolong, Guo, Hanyi, Xu, Haojun, Li, Caizhi, He, Weifeng, Pei, Binbin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.05.2023
Predmet:	Composite materials Deep learning Non-destructive testing Ultrasonic flaw detection PrDiMP Deep learning Composite materials D3S DiMP Non-destructive testing Ultrasonic flaw detection ATOM KCF
ISSN:	0003-682X, 1872-910X
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