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Title: Using Deep Machine Learning to Conduct Object-Based Identification and Motion Detection on Safeguards Video Surveillance

Abstract

Video surveillance is one of the core monitoring technologies used by the International Atomic Energy Agency (IAEA) Department of Safeguards at safeguarded nuclear facilities worldwide. Current IAEA image-review software has functions for scene-change detection, black image detection and missing scene analysis, but their capabilities are not optimum. The current workflow for the detection of safeguards relevant events heavily depends on inspectors’ laborious visual examination of surveillance videos, which is a time-consuming process and prone to errors. To improve the accuracy of the process and reduce inspectors’ burden, the paper proposes using deep machine learning to detect objects of interest in video streams and to conduct object-based motion detection. The hypothesis of this work is that deep machine learning will reduce the burden on inspectors and reduce errors by automatically locating and identifying objects of interest in video streams. Objects of interest include casks and fuel assemblies that are typically monitored by inspectors. The algorithm being developed in this work is based on a computationally efficient deep machine learning algorithm – You Only Look Once (YOLO) – but is further devised to address specific challenges related to the operation of nuclear facilities. The developed model (which is called YOLO-NSG – YOLOmore » for Nuclear Safeguards) is evaluated with data sets collected at the test facilities at Brookhaven National Laboratory (BNL) and Sandia National Laboratories (SNL). The initial focus of the research is for application at safeguarded nuclear reactors, such as pressurized heavy-water reactors, where video surveillance is broadly deployed, but can be extended to other use cases of nuclear safeguards. The detailed structure of YOLO-NSG model is introduced and the test results are reported in the paper.« less

Authors:
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
OSTI Identifier:
1466602
Report Number(s):
BNL-207942-2018-COPA
DOE Contract Number:  
SC0012704
Resource Type:
Conference
Resource Relation:
Conference: Symposium on International Safeguards: Building Future Safeguards Capabilities, Vienna, Austria, 11/5/2018 - 11/8/2018
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Cui, Yonggang. Using Deep Machine Learning to Conduct Object-Based Identification and Motion Detection on Safeguards Video Surveillance. United States: N. p., 2018. Web.
Cui, Yonggang. Using Deep Machine Learning to Conduct Object-Based Identification and Motion Detection on Safeguards Video Surveillance. United States.
Cui, Yonggang. Mon . "Using Deep Machine Learning to Conduct Object-Based Identification and Motion Detection on Safeguards Video Surveillance". United States. https://www.osti.gov/servlets/purl/1466602.
@article{osti_1466602,
title = {Using Deep Machine Learning to Conduct Object-Based Identification and Motion Detection on Safeguards Video Surveillance},
author = {Cui, Yonggang},
abstractNote = {Video surveillance is one of the core monitoring technologies used by the International Atomic Energy Agency (IAEA) Department of Safeguards at safeguarded nuclear facilities worldwide. Current IAEA image-review software has functions for scene-change detection, black image detection and missing scene analysis, but their capabilities are not optimum. The current workflow for the detection of safeguards relevant events heavily depends on inspectors’ laborious visual examination of surveillance videos, which is a time-consuming process and prone to errors. To improve the accuracy of the process and reduce inspectors’ burden, the paper proposes using deep machine learning to detect objects of interest in video streams and to conduct object-based motion detection. The hypothesis of this work is that deep machine learning will reduce the burden on inspectors and reduce errors by automatically locating and identifying objects of interest in video streams. Objects of interest include casks and fuel assemblies that are typically monitored by inspectors. The algorithm being developed in this work is based on a computationally efficient deep machine learning algorithm – You Only Look Once (YOLO) – but is further devised to address specific challenges related to the operation of nuclear facilities. The developed model (which is called YOLO-NSG – YOLO for Nuclear Safeguards) is evaluated with data sets collected at the test facilities at Brookhaven National Laboratory (BNL) and Sandia National Laboratories (SNL). The initial focus of the research is for application at safeguarded nuclear reactors, such as pressurized heavy-water reactors, where video surveillance is broadly deployed, but can be extended to other use cases of nuclear safeguards. The detailed structure of YOLO-NSG model is introduced and the test results are reported in the paper.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Conference:
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