Automated crack feature detection in remote visual inspection of nuclear power plant structures

Research output: Contribution to conferencePoster

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Abstract

Assurance of the normal condition of components is necessary for safe continued operation. Manual visual inspection may be prone to large volume of inspection data, making the manual assessment laborious and lengthy. This project develops a decision-support tool based on deep learning to automatically detect crack features in the inspection footage of superheater tube plate upper radius region.


Original languageEnglish
Number of pages1
Publication statusPublished - 28 Jun 2023
EventNuclear Decommissioning Authority Innovation and Technology Roadshow - Glasgow, UK
Duration: 28 Jun 202328 Jun 2023
https://www.strath.ac.uk/research/advancednuclearresearchcentre/news/ndaevent28june23/

Exhibition

ExhibitionNuclear Decommissioning Authority Innovation and Technology Roadshow
Period28/06/2328/06/23
Internet address

Keywords

  • automated anomaly detection
  • nuclear power plant inspection
  • remote visual inspection (RVI)

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