Employing a U-net convolutional neural network for segmenting impact damages in optical lock-in thermography images of CFRP plates-Reference-Cited by-同舟云学术

Employing a U-net convolutional neural network for segmenting impact damages in optical lock-in thermography images of CFRP plates

Published:2020-05-15 Issue: Volume: Page:1-19
ISSN:1058-9759
Container-title:Nondestructive Testing and Evaluation
language:en
Short-container-title:Nondestructive Testing and Evaluation

Author:

Oliveira B. C. F.¹^ORCID,Seibert A. A.¹,Borges V. K.¹,Albertazzi A.¹,Schmitt R. H.²^ORCID

Affiliation:

1. Campus Universitário UFSC, Universidade Federal De Santa Catarina, Florianópolis, Brazil

2. Chair of Production Metrology and Quality Management, RWTH Aachen University, Aachen, Germany

Publisher

Informa UK Limited

Subject

General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/10589759.2020.1758099

Reference30 articles.

1. Zöcke CM Quantitative analysis of defects in composite material by means of optical lock-in thermography [dissertation]. Universität des Saarlandes; 2009.

2. Low-velocity impact damage characterization of carbon fiber reinforced polymer (CFRP) using infrared thermography

3. Ibarra-Castanedo C Quantitative subsurface defect evaluation by pulsed phase thermography: depth retrieval with the phase [dissertation]. Université Laval; 2005.

4. Exploring deep learning networks for tumour segmentation in infrared images

5. Filtered thermal contrast based technique for testing of material by infrared thermography

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