Vision-based detection algorithm for monitoring dynamic change of fire progression
Abstract Fire incidents in industrial settings often result in hundreds of worker fatalities, severe injuries, and substantial financial losses. To minimize the impact of industrial fire accidents, it is essential to establish response strategies that adapt to fire progression. This study aims to de...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01211-9 |
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| _version_ | 1850231529459941376 |
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| author | Yongyoon Suh |
| author_facet | Yongyoon Suh |
| author_sort | Yongyoon Suh |
| collection | DOAJ |
| description | Abstract Fire incidents in industrial settings often result in hundreds of worker fatalities, severe injuries, and substantial financial losses. To minimize the impact of industrial fire accidents, it is essential to establish response strategies that adapt to fire progression. This study aims to define vision-based patterns of fire events to identify multiple objects that contribute to different types of fire accidents. To achieve this, a convolutional neural network (CNN) based on deep learning is applied to detect fire events through vision-based patterns. Flames and smoke are trained as multiple objects to recognize fire event patterns, while their size and position are visualized to assess fire severity. The results offer valuable insights for industrial supervisors, academic researchers, and fire accident investigators, enhancing their understanding of fire incidents and their progression within industrial environments. This vision-based approach provides a more effective method for detecting and forecasting fire development, contributing to improved fire safety and emergency response strategies. |
| format | Article |
| id | doaj-art-16a8d4bb4a1a4f5684e63f3dea87cbc0 |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-16a8d4bb4a1a4f5684e63f3dea87cbc02025-08-20T02:03:31ZengSpringerOpenJournal of Big Data2196-11152025-05-0112111910.1186/s40537-025-01211-9Vision-based detection algorithm for monitoring dynamic change of fire progressionYongyoon Suh0Department of Industrial & Systems Engineering, Dongguk UniversityAbstract Fire incidents in industrial settings often result in hundreds of worker fatalities, severe injuries, and substantial financial losses. To minimize the impact of industrial fire accidents, it is essential to establish response strategies that adapt to fire progression. This study aims to define vision-based patterns of fire events to identify multiple objects that contribute to different types of fire accidents. To achieve this, a convolutional neural network (CNN) based on deep learning is applied to detect fire events through vision-based patterns. Flames and smoke are trained as multiple objects to recognize fire event patterns, while their size and position are visualized to assess fire severity. The results offer valuable insights for industrial supervisors, academic researchers, and fire accident investigators, enhancing their understanding of fire incidents and their progression within industrial environments. This vision-based approach provides a more effective method for detecting and forecasting fire development, contributing to improved fire safety and emergency response strategies.https://doi.org/10.1186/s40537-025-01211-9Fire incidentsVision-based patternConvolution neural networkFire progressionPatterns of fire events |
| spellingShingle | Yongyoon Suh Vision-based detection algorithm for monitoring dynamic change of fire progression Journal of Big Data Fire incidents Vision-based pattern Convolution neural network Fire progression Patterns of fire events |
| title | Vision-based detection algorithm for monitoring dynamic change of fire progression |
| title_full | Vision-based detection algorithm for monitoring dynamic change of fire progression |
| title_fullStr | Vision-based detection algorithm for monitoring dynamic change of fire progression |
| title_full_unstemmed | Vision-based detection algorithm for monitoring dynamic change of fire progression |
| title_short | Vision-based detection algorithm for monitoring dynamic change of fire progression |
| title_sort | vision based detection algorithm for monitoring dynamic change of fire progression |
| topic | Fire incidents Vision-based pattern Convolution neural network Fire progression Patterns of fire events |
| url | https://doi.org/10.1186/s40537-025-01211-9 |
| work_keys_str_mv | AT yongyoonsuh visionbaseddetectionalgorithmformonitoringdynamicchangeoffireprogression |