Automated crack feature detection in remote visual inspection of nuclear power plant structures
Fei, Zhouxiang and West, Graeme and Murray, Paul and Dobie, Gordon (2023) Automated crack feature detection in remote visual inspection of nuclear power plant structures. In: Nuclear Decommissioning Authority Innovation and Technology Roadshow, 2023-06-28 - 2023-06-28, Glasgow, UK.
Preview |
Text.
Filename: Fei_etal_NDAITR_2023_Automated_crack_feature_detection_in_remote_visual.pdf
Final Published Version License: Strathprints license 1.0 Download (1MB)| Preview |
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.
ORCID iDs
Fei, Zhouxiang ORCID: https://orcid.org/0000-0002-5003-3949, West, Graeme ORCID: https://orcid.org/0000-0003-0884-6070, Murray, Paul ORCID: https://orcid.org/0000-0002-6980-9276 and Dobie, Gordon ORCID: https://orcid.org/0000-0003-3972-5917;-
-
Item type: Conference or Workshop Item(Poster) ID code: 86530 Dates: DateEvent28 June 2023PublishedSubjects: Science > Physics > Nuclear and particle physics. Atomic energy. Radioactivity Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > EnergyDepositing user: Pure Administrator Date deposited: 18 Aug 2023 00:48 Last modified: 11 Nov 2024 17:09 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/86530
CORE (COnnecting REpositories)