FFDNet: An End-to-End Lightweight Real-Time Fire Detection Model for Forest Fire Environments

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Fire detection accuracy is enhanced by using an end-to-end model that operates in real-time, effectively identifying forest fire threats.
The model achieves detection with a low false positive rate, ensuring reliability in various fire-prone environments.
Using lightweight architecture, the fire detection model remains efficient enough for deployment on devices with limited computational resources.
Implications of this model support proactive measures in forest fire prevention and management, especially in vulnerable areas.

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Authors

Qian Yu

Central South University of Forestry and Technology

Gui Zhang

Central South University of Forestry and Technology

Ying Wang

Central South University of Forestry and Technology

Journals

Physics and Chemistry of the Earth Parts A/B/C

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Central South University

Central South University of Forestry and Technology

Hunan University of Technology

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Yu et al. (Sun,) studied this question.

synapsesocial.com/papers/69a7617dc6e9836116a2f7ff — DOI: https://doi.org/10.1016/j.pce.2026.104325

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Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction· 2018 · 216 citations

Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation· 2014 · 31,682 citations
Deep Learning-Based Multistage Fire Detection System and Emerging Direction· 2024 · 14 citations
YOLO9000: Better, Faster, Stronger· 2017 · 18,912 citations
SSD: Single Shot MultiBox Detector· 2016 · 20,861 citations
Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction· 2018 · 216 citations

FFDNet: An End-to-End Lightweight Real-Time Fire Detection Model for Forest Fire Environments

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Cite this study

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