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A new multi-sensor fire detection method based on LSTM networks with environmental information fusion

  • S.I.: Evolutionary Computation based Methods and Applications for Data Processing
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Abstract

Multi-sensor fire detection has been widely used, which allows monitoring multiple environmental indicators. However, most multi-sensor detection methods detect fires only by comparing the measurements of environmental indicators at each detection time with the preset thresholds. It is prone to fire false alarms due to neglecting the time series characteristics of environmental information. To improve the robustness and accuracy of fire detection, this paper proposes a new multi-sensor fire detection method based on long short-term memory (LSTM) networks, named EIF-LSTM. EIF-LSTM integrates environmental information fusion, which is divided into two steps. First, EIF-LSTM extracts the time series characteristics of the monitoring environment by processing multi-sensor time series readings, including environmental indicator variation information and environmental level information. Second, the normalized multi-sensor time series readings, environmental indicator variation information and environmental level information are fused together for fire prediction. The LSTM network realizes the extraction of environmental time series characteristics due to its ability to learn long-term dependencies. The addition of two kinds of time series information increases the detection dimension and enhances the fusion effect. Experimental results on a real-world fire dataset show that EIF-LSTM is capable of achieving state-of-the-art detection performance.

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Funding

This work was supported by the National Natural Science Foundation of China under Grants of 61762029 and 61972237, Guilin Science and Technology Development Program under Grant of 20190211-20 and the Natural Science Foundation of Shandong Province under grant of ZR2019MF017.

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Authors and Affiliations

  1. School of Business, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China

    Pingshan Liu & Pingchuan Xiang

  2. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, Shandong, China

    Dianjie Lu

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  1. Pingshan Liu
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  2. Pingchuan Xiang
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  3. Dianjie Lu
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Correspondence to Pingchuan Xiang.

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Liu, P., Xiang, P. & Lu, D. A new multi-sensor fire detection method based on LSTM networks with environmental information fusion. Neural Comput & Applic 35, 25275–25289 (2023). https://doi.org/10.1007/s00521-023-08709-4

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