A Systematic Literature Review of Vision-Based Fire Detection, Prediction and Forecasting
The primary method used by conventional fire detection systems is sensor-based detection, which has limitations in terms of accuracy and detection time. Traditional approaches and techniques could be improved by the latest advancements in computer vision-based technologies for fire prediction and de...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
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| Online Access: | http://journalarticle.ukm.my/26754/1/13.pdf http://journalarticle.ukm.my/26754/ https://www.ukm.my/jkukm/volume-3701-2025/ |
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| Summary: | The primary method used by conventional fire detection systems is sensor-based detection, which has limitations in terms of accuracy and detection time. Traditional approaches and techniques could be improved by the latest advancements in computer vision-based technologies for fire prediction and detection. Consequently, this paper aims to provide a comprehensive literature analysis of earlier research on fire detection and prediction using the computer vision techniques. The Preferred Reporting Items for Systemic Reviews and Meta-Analyses, or PRISMA 2020, are applied in this systematic review. Three databases such as the Web of Science, Scopus, and IEEE were searched for pertinent publications to include in this review for this study. The systematic review reveals that existing studies predominantly focused on fire flame rather than smoke detection. Moreover, the majority of research has centered on forest fires in the particular context of occurrence, neglecting indoor or interior environments. Video surveillance systems emerge as the primary source of hardware and datasets utilized in these investigations. Notably, convolutional neural networks (CNNs) stand out as the most frequently employed deep learning approach for classification purposes. The systematic review clarifies the state of fire detection research using computer vision techniques by combining data from several academic sources. Through a systematic approach, this study contributes a deeper understanding of the opportunity and challenges in leveraging vision-based technologies for fire detection and prediction. |
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