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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Veröffentlicht in:	Applied acoustics Jg. 207; S. 109363
Hauptverfasser:	Hu, Qichun, Wei, Xiaolong, Guo, Hanyi, Xu, Haojun, Li, Caizhi, He, Weifeng, Pei, Binbin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.05.2023
Schlagworte:	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
Online-Zugang:	Volltext
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