A Hybrid Approach for Thermographic Imaging with Deep Learning

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

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4277-4281
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period04.05.202008.05.2020

Keywords

  • deep learning
  • non-destructive testing
  • Thermography
  • u-net
  • virtual waves

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