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Attention structured Pattern Deep Learning for region induction thermography NDT

  • University of Electronic Science and Technology of China

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

Abstract

Induction Thermography is a crucial nondestructive testing technology which has a rapidly increasing range of applications for crack detection. A series of studies have been carried out in thermal sequences processing algorithms. However, these methods often suffer from the complex surface conditions of experiment components. In this study, we propose an end-to-end attention structured pattern deep learning method to achieve precise crack detection and localization. The proposed method integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on welding line cracks have shown attractive performance and verified the efficacy of the proposed structure.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE Far East NDT New Technology and Application Forum, FENDT 2019
EditorsChunguang Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages81-84
Number of pages4
ISBN (Electronic)9781728134109
DOIs
StatePublished - Jun 2019
Externally publishedYes
Event2019 IEEE Far East NDT New Technology and Application Forum, FENDT 2019 - Qingdao, China
Duration: 24 Jun 201927 Jun 2019

Publication series

NameProceedings of 2019 IEEE Far East NDT New Technology and Application Forum, FENDT 2019

Conference

Conference2019 IEEE Far East NDT New Technology and Application Forum, FENDT 2019
Country/TerritoryChina
CityQingdao
Period24/06/1927/06/19

Keywords

  • attention model
  • induction thermography
  • non-destructive testing
  • pattern deep region learning

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