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Devereaux_etal_NT_2020_Automated_object_detection_for_visual_inspection_of_nuclear_reactor.pdf
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🔗 Source: strathprints.strath.ac.uk
📊 Size: 4.46 MB
📄 Pages: 28 pages
⬇️ Downloads: 321

Summary

Automated object detection is applied to visual inspection of nuclear reactor cores, specifically Advanced Gas-Cooled Reactors (AGR), to identify reference objects with known dimensions. Two approaches are evaluated: a multistep method using mathematical morphology and a semantic segmentation technique using deep neural networks (DNN). The semantic segmentation method performs better in detecting brick interface keyways in AGR inspection images, but requires more initial effort to train and lacks transparency. The mathematical morphology approach is more transparent but less accurate. Both methods support visual inspection as decision support tools, retaining human analysts in the decision-making process.

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Description

Automated object detection is applied to visual inspection of nuclear reactor cores, specifically Advanced Gas-Cooled Reactors (AGR), to identify reference...

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  • File Format: PDF
  • File Size: 4.46 MB
  • Pages: 28
  • Language: EN
  • Total Downloads: 321
  • Last Updated: 6 days ago

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