Advanced video analysis for automated feature identification on Special Nuclear Materials (SNM) packages
Lead Research Organisation:
University of Strathclyde
Department Name: Electronic and Electrical Engineering
Abstract
Sellafield Ltd is responsible for the storage of Special Nuclear Materials (SNM) that are a legacy of 60 years of reprocessing activities on the Sellafield site.
To provide confidence that SNM packages remain safe for continued storage within Sellafield's stores, it is necessary to closely monitor the exterior surface of the packages whilst they are in their storage location (in-situ). Such monitoring will inform the selection of packages for ex-situ inspection.
The aim of this research is to develop new image and video processing algorithms which can be used to analyse existing and future SNM inspection videos to automatically detect and quantify the condition of each SNM package that undergoes inspection. Specifically, techniques will be designed to detect and quantify the following characteristics of SNM packages:
-Dimensions of any dents, scratches or scuffs
-Evidence of cracking
-Original manufacturing defects
-Evidence of corrosion/colour change
If successful, new methods will then be designed to perform batch analysis of all SNM containers to undergo inspection with the aim of identifying those at most and least risk of requiring ex-situ inspection and, possibly, some form of re-processing and/or re-packaging. The explicability of the feature detection algorithms will also be assessed with the aim of designing explicable AI based solutions.
To provide confidence that SNM packages remain safe for continued storage within Sellafield's stores, it is necessary to closely monitor the exterior surface of the packages whilst they are in their storage location (in-situ). Such monitoring will inform the selection of packages for ex-situ inspection.
The aim of this research is to develop new image and video processing algorithms which can be used to analyse existing and future SNM inspection videos to automatically detect and quantify the condition of each SNM package that undergoes inspection. Specifically, techniques will be designed to detect and quantify the following characteristics of SNM packages:
-Dimensions of any dents, scratches or scuffs
-Evidence of cracking
-Original manufacturing defects
-Evidence of corrosion/colour change
If successful, new methods will then be designed to perform batch analysis of all SNM containers to undergo inspection with the aim of identifying those at most and least risk of requiring ex-situ inspection and, possibly, some form of re-processing and/or re-packaging. The explicability of the feature detection algorithms will also be assessed with the aim of designing explicable AI based solutions.
People |
ORCID iD |
| Paul Murray (Primary Supervisor) | |
| Brandon Calder (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S022821/1 | 01/10/2019 | 31/03/2028 | |||
| 2897614 | Studentship | EP/S022821/1 | 04/09/2023 | 03/09/2027 | Brandon Calder |