A Hybrid Approach for Thermographic Imaging with Deep Learning

Peter Kovacs, Bernhard Lehner, Gregor Thummerer, Gunther Mayr, Peter Burgholzer, Mario Huemer

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)

Abstract

We propose a hybrid method for reconstructing thermographic images by combining the recently developed virtual wave concept with deep neural networks. The method can be used to detect defects inside materials in a non-destructive way. We propose two architectures along with a thorough evaluation that shows a substantial improvement compared to state-of-the-art reconstruction procedures. The virtual waves are invariant of the thermal diffusivity property of the material. Consequently, we can use extremely compact architectures that require relatively little training data, and have very fast loss convergence. As a supplement of the paper [1], we provide the MATLAB and Python implementations along with the data set comprising 40,000 simulated temperature measurement images in total, and their corresponding defect locations. Thus, the presented results are completely reproducible.

OriginalspracheEnglisch
Titel2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten4277-4281
Seitenumfang5
ISBN (elektronisch)9781509066315
DOIs
PublikationsstatusVeröffentlicht - Mai 2020
Veranstaltung2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spanien
Dauer: 4 Mai 20208 Mai 2020

Publikationsreihe

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Band2020-May
ISSN (Print)1520-6149

Konferenz

Konferenz2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
LandSpanien
OrtBarcelona
Zeitraum04.05.202008.05.2020

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