Open Access

ARTICLE

Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network

A. K. Z Rasel Rahman¹, S. M. Nabil Sakif¹, Niloy Sikder¹, Mehedi Masud², Hanan Aljuaid³, Anupam Kumar Bairagi^1,*

1 Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
2 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
3 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), Riyadh 11671, Saudi Arabia

* Corresponding Author: Anupam Kumar Bairagi. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3259-3277. https://doi.org/10.32604/iasc.2023.030142

Received 19 March 2022; Accepted 04 May 2022; Issue published 17 August 2022

Abstract

Disasters may occur at any time and place without little to no presage in advance. With the development of surveillance and forecasting systems, it is now possible to forebode the most life-threatening and formidable disasters. However, forest fires are among the ones that are still hard to anticipate beforehand, and the technologies to detect and plot their possible courses are still in development. Unmanned Aerial Vehicle (UAV) image-based fire detection systems can be a viable solution to this problem. However, these automatic systems use advanced deep learning and image processing algorithms at their core and can be tuned to provide accurate outcomes. Therefore, this article proposed a forest fire detection method based on a Convolutional Neural Network (CNN) architecture using a new fire detection dataset. Notably, our method also uses separable convolution layers (requiring less computational resources) for immediate fire detection and typical convolution layers. Thus, making it suitable for real-time applications. Consequently, after being trained on the dataset, experimental results show that the method can identify forest fires within images with a 97.63% accuracy, 98.00% F1 Score, and 80% Kappa. Hence, if deployed in practical circumstances, this identification method can be used as an assistive tool to detect fire outbreaks, allowing the authorities to respond quickly and deploy preventive measures to minimize damage.

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Keywords

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