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Research on Multi-Sensor Fusion Indoor Fire Perception Algorithm Based on Improved TCN.

Yang Li1, Yanmang Su1,2, Xiangye Zeng1,2

  • 1School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, China.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

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This study introduces a novel multi-sensor fusion algorithm for rapid and accurate indoor fire perception. The proposed method enhances fire classification accuracy and detection speed, significantly improving fire prediction systems.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Indoor fires pose significant global risks, necessitating advanced detection methods.
  • Current fire perception systems require improvements in speed and accuracy.

Purpose of the Study:

  • To develop an indoor fire perception algorithm utilizing multi-sensor fusion.
  • To enhance the speed and accuracy of fire detection and classification.

Main Methods:

  • Feature extraction using an improved Temporal Convolutional Network (TCN).
  • Feature dimension reduction via Adaptive Average Pooling (AAP).
  • Fire classification employing a Support Vector Machine (SVM) classifier.

Main Results:

Keywords:
AAPSVMTCNfire perceptionmulti-sensor fusiontrend extraction

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  • The proposed algorithm achieved over 2.5% higher accuracy in fire classification.
  • Detection speed was improved by more than 15% compared to TCN, BP, and LSTM.
  • Demonstrated superior performance over existing methods like TCN, Back Propagation (BP) neural network, and Long Short-Term Memory (LSTM).

Conclusions:

  • The multi-sensor fusion algorithm enables quick and accurate indoor fire perception.
  • This advancement holds significant value for enhancing current fire prediction system performance.