Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning
Loading...
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Mdpi
Open Access Color
GOLD
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning.
Description
Keywords
artificial intelligence, deep learning, detection, fire, flame, forest fire, smoke, wildfire, flame, detection, deep learning, Information technology, artificial intelligence, T58.5-58.64, fire, forest fire
Turkish CoHE Thesis Center URL
Fields of Science
Citation
WoS Q
Q2
Scopus Q
Q2

OpenCitations Citation Count
N/A
Source
Information
Volume
15
Issue
9
Start Page
538
End Page
PlumX Metrics
Citations
Scopus : 28
Captures
Mendeley Readers : 56
SCOPUS™ Citations
28
checked on Jan 31, 2026
Web of Science™ Citations
17
checked on Jan 31, 2026
Page Views
4
checked on Jan 31, 2026
Downloads
66
checked on Jan 31, 2026
Google Scholar™


