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Vision Sensor-Based Real-Time Fire Detection in Resource-Constrained IoT Environments.

Hikmat Yar1, Tanveer Hussain1, Zulfiqar Ahmad Khan1

  • 1Sejong University, Seoul 143-747, Republic of Korea.

Computational Intelligence and Neuroscience
|December 31, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a lightweight convolutional neural network (CNN) for efficient, real-time fire detection in Internet of Things (IoT) environments. The novel CNN model achieves high accuracy with reduced resources, outperforming existing methods.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Environmental Monitoring

Background:

  • Real-time fire detection is crucial for mitigating societal, ecological, and economic losses.
  • Internet of Things (IoT) environments present challenges for accurate fire detection due to resource constraints.
  • Early fire detection and automatic response systems are significant for effective fire management.

Purpose of the Study:

  • To develop a novel, lightweight convolutional neural network (CNN) framework for early fire detection.
  • To create a model suitable for resource-constrained devices in IoT settings.
  • To enhance accuracy and reduce computational load for fire detection systems.

Main Methods:

  • A novel lightweight CNN architecture inspired by VGG16, featuring reduced parameters, input size, and inference time.

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  • Utilization of small-size uniform convolutional filters for capturing fine image details.
  • Sequential increase in the number of channels for effective feature extraction.
  • Evaluation on the Foggia dataset and a newly created diverse, real-world fire detection dataset.

    • Main Results:

    • The proposed lightweight CNN model demonstrated superior performance compared to state-of-the-art methods.
    • Achieved higher accuracy and lower false-positive rates in fire detection.
    • Significantly reduced model size and running time, indicating efficiency.
    • Robustness and feasibility for real-world deployment in challenging conditions.

    Conclusions:

    • The developed lightweight CNN framework offers an effective solution for early fire detection in resource-constrained IoT environments.
    • The model's efficiency in terms of accuracy, size, and speed makes it suitable for practical applications.
    • This approach addresses key challenges in real-time fire detection, paving the way for improved safety and management systems.