Safeguards-related event detection in surveillance video using semi-supervised learning approach
We develop a deep learning model employing a semi-supervised learning approach, which can detect automatically safeguards-related events in nuclear facility from surveillance video. Our model is designed after a comprehensive analysis of the trends in artificial intelligence-based models to identify...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Nuclear Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573324004546 |
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| _version_ | 1832576454101041152 |
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| author | Se-Hwan Park Byung-Hee Won Seong-Kyu Ahn |
| author_facet | Se-Hwan Park Byung-Hee Won Seong-Kyu Ahn |
| author_sort | Se-Hwan Park |
| collection | DOAJ |
| description | We develop a deep learning model employing a semi-supervised learning approach, which can detect automatically safeguards-related events in nuclear facility from surveillance video. Our model is designed after a comprehensive analysis of the trends in artificial intelligence-based models to identify abnormal events in video. Our model incorporates a reconstruction module and a prediction module independently. The reconstruction module is trained to generate video frames within a sliding window, while the prediction module is trained to predict future motion feature based on the motion features within the video frames in a sliding window. Each module utilizes an autoencoder with a memory module positioned between an encoder and an decoder of the autoencoder. We evaluate the model's performance using a benchmark dataset and a self-produced dataset obtained from facility related to pyroprocessing. Our model's performanace is comparable to or superior to that of the prevous models from the benchmark dataset analysis, and all the abnormal events can be detected without false positive error from the self-produced dataset analysis. |
| format | Article |
| id | doaj-art-09a3e11da7ee4d07a76a87ba7fad0d49 |
| institution | Kabale University |
| issn | 1738-5733 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj-art-09a3e11da7ee4d07a76a87ba7fad0d492025-01-31T05:11:07ZengElsevierNuclear Engineering and Technology1738-57332025-02-01572103206Safeguards-related event detection in surveillance video using semi-supervised learning approachSe-Hwan Park0Byung-Hee Won1Seong-Kyu Ahn2Corresponding author.; Advanced Fuel Cycle Technology Division, Korea Atomic Energy Research Institute, Yuseong, Daejeon, 34057, Republic of KoreaAdvanced Fuel Cycle Technology Division, Korea Atomic Energy Research Institute, Yuseong, Daejeon, 34057, Republic of KoreaAdvanced Fuel Cycle Technology Division, Korea Atomic Energy Research Institute, Yuseong, Daejeon, 34057, Republic of KoreaWe develop a deep learning model employing a semi-supervised learning approach, which can detect automatically safeguards-related events in nuclear facility from surveillance video. Our model is designed after a comprehensive analysis of the trends in artificial intelligence-based models to identify abnormal events in video. Our model incorporates a reconstruction module and a prediction module independently. The reconstruction module is trained to generate video frames within a sliding window, while the prediction module is trained to predict future motion feature based on the motion features within the video frames in a sliding window. Each module utilizes an autoencoder with a memory module positioned between an encoder and an decoder of the autoencoder. We evaluate the model's performance using a benchmark dataset and a self-produced dataset obtained from facility related to pyroprocessing. Our model's performanace is comparable to or superior to that of the prevous models from the benchmark dataset analysis, and all the abnormal events can be detected without false positive error from the self-produced dataset analysis.http://www.sciencedirect.com/science/article/pii/S1738573324004546Deep learningSemi-supervised learningSafeguardsPyroprocessing |
| spellingShingle | Se-Hwan Park Byung-Hee Won Seong-Kyu Ahn Safeguards-related event detection in surveillance video using semi-supervised learning approach Nuclear Engineering and Technology Deep learning Semi-supervised learning Safeguards Pyroprocessing |
| title | Safeguards-related event detection in surveillance video using semi-supervised learning approach |
| title_full | Safeguards-related event detection in surveillance video using semi-supervised learning approach |
| title_fullStr | Safeguards-related event detection in surveillance video using semi-supervised learning approach |
| title_full_unstemmed | Safeguards-related event detection in surveillance video using semi-supervised learning approach |
| title_short | Safeguards-related event detection in surveillance video using semi-supervised learning approach |
| title_sort | safeguards related event detection in surveillance video using semi supervised learning approach |
| topic | Deep learning Semi-supervised learning Safeguards Pyroprocessing |
| url | http://www.sciencedirect.com/science/article/pii/S1738573324004546 |
| work_keys_str_mv | AT sehwanpark safeguardsrelatedeventdetectioninsurveillancevideousingsemisupervisedlearningapproach AT byungheewon safeguardsrelatedeventdetectioninsurveillancevideousingsemisupervisedlearningapproach AT seongkyuahn safeguardsrelatedeventdetectioninsurveillancevideousingsemisupervisedlearningapproach |