Convolutional Graph Thermography for Subsurface Defect Detection in Polymer Composites

Infrared thermography for quality assessment of polymer composites has gained increasing attention with the development of various thermographic data analysis methods. However, low-quality thermal images containing noise and inhomogeneous backgrounds restrict the defect detection performance of thes...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 71; S. 1
Hauptverfasser:	Liu, Kaixin, Yu, Qing, Liu, Yi, Yang, Jianguo, Yao, Yuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Background noise Carbon fiber reinforced plastics Convolution convolutional autoencoder Data analysis Decoding defect detection Defects Feature extraction Fiber reinforced polymers Image quality Infrared imaging infrared thermography manifold learning noise reduction Non-destructive testing Nonhomogeneous media Polymer matrix composites Polymers Quality assessment Temperature measurement Thermal imaging Thermography Training
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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