Object detection in a framework for automated nuclear waste classification

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper, we present a new framework for automatically triaging nuclear waste classification inside a nuclear cell for decommissioning. The process of decommissioning includes a large amount of human involvement for decision making, physical inspections and even lifting and relocating radioactive waste items. The current process accounts for risks like close human contact with radioactive material for extended periods of time, and errors based on operator knowledge rather than automated detection systems. The aims of this new framework are to reduce cost and speed up the sort and segregation process by providing a list of expected waste items, their location within the cell, and expected waste classification autonomously. We aim to reduce the reliance on the subjectivity of human decisions by capturing, formalizing and codifying their knowledge and experience and reducing the potential for errors arising from reliance on individuals. This paper focuses on the design and description of the framework and demonstration of the first step of the framework through a case study drawn from a mockup of a nuclear cell. We perform planar segmentation and cylinder detection on a point cloud dataset using RANSAC based methods to, firstly, distinguish indoor walls from objects for processing, and to detect objects and their estimated parameters.
Original languageEnglish
Number of pages10
Publication statusPublished - 14 Jun 2021
Event12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021) - Virtual
Duration: 14 Jun 202116 Jun 2021
Conference number: 12
https://www.ans.org/meetings/am2021/

Conference

Conference12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021)
Abbreviated titleNPIC HMIT 2021
Period14/06/2116/06/21
Internet address

Keywords

  • cylinder fitting
  • planar segmentation
  • point cloud
  • RANSAC
  • nuclear waste classification

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