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BiLSTM-LN-SA: A Novel Integrated Model with Self-Attention for Multi-Sensor Fire Detection.
Zhaofeng He1, Yu Si2, Liyuan Yang3
1School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.
Sensors (Basel, Switzerland)
|October 29, 2025
Summary
A new BiLSTM-LN-SA model improves multi-sensor fire detection by capturing time-series data dependencies. This advanced fire detection technology significantly reduces false alarms and enhances adaptability in complex environments.
Area of Science:
- Engineering
- Computer Science
- Artificial Intelligence
Background:
- Multi-sensor fire detection is crucial but challenged by high false alarm rates.
- Existing methods struggle with complex environments and deep time-series data analysis.
Purpose of the Study:
- To develop a novel fire detection model, BiLSTM-LN-SA, for enhanced robustness and accuracy.
- To improve adaptability and generalization capabilities in diverse environmental scenarios.
Main Methods:
- Integrated Bidirectional Long Short-Term Memory (BiLSTM) for time-series feature extraction.
- Incorporated Layer Normalization (LN) to mitigate feature distribution shifts.
- Utilized Self-Attention (SA) mechanism for dynamic feature recalibration and fusion.
Main Results:
- Achieved 98.38% test accuracy, 0.98 F1-score, and 0.99 AUC.
- Demonstrated significantly lower false positive (1.50%) and false negative (1.85%) rates.
- Outperformed existing methods like EIF-LSTM, rTPNN, and MLP.
Conclusions:
- The BiLSTM-LN-SA model offers superior performance and reliability in multi-sensor fire detection.
- Layer Normalization and Self-Attention are key components for adaptability and feature fusion.
- The model shows strong generalization capability for practical applications in varied environments.
