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Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection.

Mira Jeong1, MinJi Park1, Jaeyeal Nam1

  • 1Department of Computer Engineering, Keimyung University, Daegu 42601, Korea.

Sensors (Basel, Switzerland)
|September 30, 2020
PubMed
Summary

This study introduces a lightweight deep learning model for wildfire smoke detection using a teacher-student framework. The enhanced model reduces parameters and processing time while maintaining high detection accuracy, improving early wildfire detection capabilities.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Environmental Monitoring

Background:

  • Wildfire detection is crucial, with camera-based systems offering cost-effectiveness but struggling to differentiate smoke from fog/clouds.
  • Existing deep learning methods for smoke detection often require heavy models, hindering real-time application.
  • Accurate and efficient wildfire smoke detection is essential for timely response and mitigation.

Purpose of the Study:

  • To develop an efficient deep learning model for wildfire smoke detection that overcomes the limitations of existing methods.
  • To improve the accuracy and speed of camera-based wildfire smoke detection systems.
  • To reduce the computational load of deep learning models for real-time wildfire smoke detection.

Main Methods:

Keywords:
LSTMYOLOv3smoke-tubestudent LSTMteacher-student frameworkwildfire smoke

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  • A You-Only-Look-Once (YOLO) detector was combined with a long short-term memory (LSTM) classifier to analyze spatial and temporal smoke characteristics.
  • A teacher-student framework was applied to a deep LSTM model to create a shallower, more efficient student model.
  • The student LSTM model was designed with reduced layers and cells while aiming to preserve the performance of the original deep LSTM.

    • Main Results:

    • The proposed teacher-student framework significantly reduced the number of parameters by up to 8.4-fold compared to the deep LSTM.
    • The student model demonstrated a faster processing time, crucial for real-time detection applications.
    • The lightweight student LSTM model maintained comparable detection performance to the deep LSTM on a wildfire benchmark dataset.

    Conclusions:

    • The teacher-student framework effectively creates a lightweight yet high-performing LSTM model for wildfire smoke detection.
    • This approach enhances the feasibility of real-time, low-cost camera-based wildfire detection systems.
    • The method offers a promising solution for improving early wildfire detection and response.