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FireMamba: An efficient visual representation learning framework for fire detection
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DOI：10.1016/j.aej.2025.10.001.png)
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Abstract
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• An efficient and memory-optimized fire detection model based on a bidirectional SSM backbone. • MSAM fuses multiscale cues to emphasize fire regions in diverse scenes. • Ablation study highlights contributions of SSM, Fire blocks, and MSAM modules. • Achieves highest accuracy on DVF, Foggia, and DF fire detection benchmarks.
Keywords:
Pattern recognition
Fire detection
FireMamba
Surveillance data
Deep learning
Computer vision
Vision Mamba
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