Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning

被引:7
作者
Ozel, Berk [1 ]
Alam, Muhammad Shahab [2 ]
Khan, Muhammad Umer [1 ]
机构
[1] Atilim Univ, Dept Mechatron Engn, TR-06830 Ankara, Turkiye
[2] Gebze Tech Univ, Def Technol Inst, TR-41400 Gebze, Turkiye
关键词
artificial intelligence; deep learning; detection; fire; flame; forest fire; smoke; wildfire; DYNAMIC TEXTURE ANALYSIS; WILDFIRE SMOKE DETECTION; COMPUTER VISION; DETECTION ALGORITHM; SENSOR NETWORK; VIDEO; RECOGNITION; SYSTEM; SEGMENTATION; EXTRACTION;
D O I
10.3390/info15090538
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:33
相关论文
共 202 条
[51]   Machine Vision Based Fire Detection Techniques: A Survey [J].
Geetha, S. ;
Abhishek, C. S. ;
Akshayanat, C. S. .
FIRE TECHNOLOGY, 2021, 57 (02) :591-623
[52]   CT-Fire: a CNN-Transformer for wildfire classification on ground and aerial images [J].
Ghali, Rafik ;
Akhloufi, Moulay A. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2023, 44 (23) :7390-7415
[53]   Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction [J].
Ghali, Rafik ;
Akhloufi, Moulay A. .
FIRE-SWITZERLAND, 2023, 6 (05)
[54]   Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation [J].
Ghali, Rafik ;
Akhloufi, Moulay A. ;
Mseddi, Wided Souidene .
SENSORS, 2022, 22 (05)
[55]   Wildfire Segmentation Using Deep Vision Transformers [J].
Ghali, Rafik ;
Akhloufi, Moulay A. ;
Jmal, Marwa ;
Mseddi, Wided Souidene ;
Attia, Rabah .
REMOTE SENSING, 2021, 13 (17)
[56]  
Ghamry KA, 2016, INT CONF UNMAN AIRCR, P1206, DOI 10.1109/ICUAS.2016.7502585
[57]   A hybrid deep learning model by combining convolutional neural network and recurrent neural network to detect forest fire [J].
Ghosh, Rajib ;
Kumar, Anupam .
MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (27) :38643-38660
[58]   Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images [J].
Govil, Kinshuk ;
Welch, Morgan L. ;
Ball, J. Timothy ;
Pennypacker, Carlton R. .
REMOTE SENSING, 2020, 12 (01)
[59]   Forest Fire Segmentation from Aerial Imagery Data Using an Improved Instance Segmentation Model [J].
Guan, Zhihao ;
Miao, Xinyu ;
Mu, Yunjie ;
Sun, Quan ;
Ye, Qiaolin ;
Gao, Demin .
REMOTE SENSING, 2022, 14 (13)
[60]   A Deep Learning Based Object Identification System for Forest Fire Detection [J].
Guede-Fernandez, Federico ;
Martins, Leonardo ;
de Almeida, Rui Valente ;
Gamboa, Hugo ;
Vieira, Pedro .
FIRE-SWITZERLAND, 2021, 4 (04)