Robust Forest Fire Detection using Deep Convolutional Neural Networks

Pilli Lalitha Kumari; Zahoora Abid; K.Jamberi; Gaurav D. Saxena

doi:10.58599/IJSMCSE.2024.1111

Authors

Pilli Lalitha Kumari Department of Computer Science and Engineering, Malla Reddy Institute of Technology, Secunderabad, Telangana, India
Zahoora Abid Department of Computer Science and Engineering, Nawab Shah Alam Khan College of Engineering and Technology, Hyderabad, Telangana, India
K.Jamberi School of Computer Science and Applications, REVA University, Bangalore, Karnataka, India
Gaurav D. Saxena Department of Computer Science and Applications, City Premier College, Nagpur, Maharashtra, India

DOI:

https://doi.org/10.58599/IJSMCSE.2024.1111

Keywords:

Forest fire detection, Deep convolutional neural networks, Aerial imagery Satellite data, Realtime monitoring, Unmanned aerial vehicles, Disaster management

Abstract

Forest fires pose significant threats to ecosystems, wildlife, and human lives, necessitating proactive measures for early detection and rapid response. This paper presents FireGuard, an efficient model for forest fire detection using deep convolutional neural networks (CNNs). Leveraging the power of deep learning and image processing techniques, FireGuard analyzes aerial imagery and satellite data to detect signs of smoke and fire outbreaks in forested areas. The model utilizes a lightweight CNN architecture optimized for real-time performance and resource-constrained environments, making it suitable for deployment on unmanned aerial vehicles (UAVs), surveillance cameras, and satellite platforms. Experimental results demonstrate the effectiveness of FireGuard in accurately identifying forest fires with high precision and recall, outperforming traditional methods and existing deep learning models. By providing early warnings of potential fire incidents, FireGuard enables timely intervention by fire fighting agencies, thereby mitigating the impact of forest fires and preserving natural habitats.

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