Efficient detection of different fire scenarios or nuisance incidents using deep learning methods

Özyurt, Osman

doi:10.1016/j.jobe.2024.109898

Journal of Building Engineering

2024

DOI: 10.1016/j.jobe.2024.109898

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Efficient detection of different fire scenarios or nuisance incidents using deep learning methods

Osman Özyurt

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2024

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Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System

Kim,

Heo,

Jin

et al. 2024

Fire

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Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems.

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Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System

Kim,

Heo,

Jin

et al. 2024

Fire

View full text Add to dashboard Cite

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Efficient detection of different fire scenarios or nuisance incidents using deep learning methods

Cited by 1 publication

References 30 publications

Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System

Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System

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