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Related Concept Videos

Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2.

Dilshod Sharobiddinov1, Hafeez Ur Rehman Siddiqui2, Adil Ali Saleem2

  • 1Department of Computer Science, Ulster University, London Branch Campus, St James' House, 10 Rosebery Avenue, Holborn, London EC1R 4TF, UK.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient edge-based system for forest fire and smoke detection using a lightweight deep learning model. The autonomous system achieves high accuracy in near real-time, enabling early wildfire mitigation in remote areas.

Keywords:
MobileNetV2autonomous detectionedge computingforest fire detectionreal-time inferencesmoke detectionwildfire monitoring

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Forest fires present significant ecological, human, and climatic threats, demanding advanced detection methods.
  • Existing detection systems (sensors, satellites, centralized analysis) face limitations like delays, false alarms, and restricted deployment.
  • Current deep learning models, while accurate, are often too resource-intensive for real-time edge applications.

Purpose of the Study:

  • To develop an autonomous, edge-based forest fire and smoke detection system.
  • To optimize a lightweight deep learning model for resource-constrained edge devices.
  • To enable real-time, localized wildfire detection and early mitigation.

Main Methods:

  • Utilized a lightweight MobileNetV2 convolutional neural network for image analysis.
  • Trained the model on a balanced dataset of fire, smoke, and non-fire images.
  • Deployed and optimized the system on a Raspberry Pi 5 edge device for autonomous operation.

Main Results:

  • Achieved a test accuracy of 97.98% for fire and smoke detection.
  • Demonstrated near real-time inference with an average latency of 0.77 seconds per frame (1.3 FPS).
  • Generated predictions locally, ensuring security and robustness without cloud dependency.

Conclusions:

  • The proposed edge-based system offers a cost-effective and scalable solution for remote wildfire monitoring.
  • Combines high accuracy, speed, and autonomous edge processing for timely fire detection.
  • Enhances environmental monitoring and early wildfire mitigation capabilities.