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Early Detection of Forest Fire Using Mixed Learning Techniques and UAV.

Varanasi Lvskb Kasyap¹, D Sumathi¹, Kumarraju Alluri¹

¹VIT-AP University, Amaravati, Andhra Pradesh 522237, India.

Computational Intelligence and Neuroscience

|July 20, 2022

Summary

This study introduces a cost-effective deep learning model for early forest fire prediction using YOLOv4 tiny and LiDAR on unmanned aerial vehicles (UAVs). The system achieves 91% accuracy, enabling real-time forest fire detection and management.

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Area of Science:

Environmental Science
Computer Science
Artificial Intelligence

Background:

Increasing frequency of forest fires due to deforestation and global warming poses significant threats to ecosystems.
Effective forest fire detection is crucial for timely management and mitigation efforts.
Technological solutions are needed to address the growing challenge of forest fire management.

Purpose of the Study:

To propose a cost-effective deep learning model for predicting forest fires.
To develop an integrated system utilizing unmanned aerial vehicles (UAVs) for forest fire detection.
To enhance forest fire management through advanced prediction techniques.

Main Methods:

A mixed learning technique combining YOLOv4 tiny and LiDAR was developed.
The model was deployed on an onboard unmanned aerial vehicle (UAV) for forest surveillance.
The system was trained on data from both dense and rainforest environments.

Main Results:

The proposed model achieved a classification time of 1.24 seconds.
High accuracy of 91% and an F1 score of 0.91 were recorded.
The system demonstrated real-time data transmission and 3D modeling capabilities.

Conclusions:

The developed deep learning model offers a cost-effective solution for forest fire prediction.
The UAV-based system provides efficient and accurate real-time forest fire detection.
This approach outperforms traditional methods in forest fire detection and prediction.