| DOI | Resolve DOI: https://doi.org/10.1016/j.firesaf.2022.103629 |
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| Author | Search for: Li, Yuchuan1; Search for: Ko, Yoon1ORCID identifier: https://orcid.org/0000-0001-9644-5108; Search for: Lee, Wonsook |
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| Affiliation | - National Research Council of Canada. Construction
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| Funder | Search for: National Research Council |
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| Format | Text, Article |
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| Physical description | 13 p. |
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| Subject | flashover; deep neural networks; dual-attention generative adversarial network; image processing; fire safety science |
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| Abstract | This paper proposes a novel hybrid model for flashover prediction in a compartment fire based on visual information from RGB images that are the same as those captured by regular vision cameras. The proposed model was developed as a research tool to study the feasibility of predicting flashover based on RGB vision data. This model consists of sub-modules with data-based methods using Deep Neural Networks and knowledge-based methods using fire safety science and mathematical model. One of the crucial features of the proposed model is enabled by a novel Dual-Attention Generative Adversarial Network that is developed in this study for the vision-to-infrared conversion process. The model and the overall procedure were validated against published test data from a compartment fire. Results show that the proposed model achieved promising performance, which also shows the potential to monitor the constant changes in a room fire through continuous processing images of flame and smoke. |
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| Publication date | 2022-07-20 |
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| Publisher | Elsevier BV |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | S0379711222001072 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 1f7a6170-f305-4225-9c33-9a3e11d0069c |
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| Record created | 2022-08-03 |
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| Record modified | 2022-08-04 |
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