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

被引:1
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 50 条
[21]   Innovations in safety management for construction sites: the role of deep learning and computer vision techniques [J].
Mohy, Amr A. ;
Bassioni, Hesham A. ;
Elgendi, Elbadr O. ;
Hassan, Tarek M. .
CONSTRUCTION INNOVATION-ENGLAND, 2024,
[22]   A review of deep learning techniques for speech processing [J].
Mehrish, Ambuj ;
Majumder, Navonil ;
Bharadwaj, Rishabh ;
Mihalcea, Rada ;
Poria, Soujanya .
INFORMATION FUSION, 2023, 99
[23]   A Review of Deep Learning Techniques in Document Image Word Spotting [J].
Lalita Kumari ;
Anuj Sharma .
Archives of Computational Methods in Engineering, 2022, 29 :1085-1106
[24]   FFireNet: Deep Learning Based Forest Fire Classification and Detection in Smart Cities [J].
Khan, Somaiya ;
Khan, Ali .
SYMMETRY-BASEL, 2022, 14 (10)
[25]   Understanding Deep Learning Techniques for Image Segmentation [J].
Ghosh, Swarnendu ;
Das, Nibaran ;
Das, Ishita ;
Maulik, Ujjwal .
ACM COMPUTING SURVEYS, 2019, 52 (04)
[26]   Review of the deep learning for food image processing [J].
Niu, Chenrui ;
Ying, Xiayang ;
Pei, Gan ;
Hu, Menghan ;
Zhai, Guangtao .
INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING, 2024, 17 (05) :15-30
[28]   Forest fire and smoke detection using deep learning-based learning without forgetting [J].
Sathishkumar, Veerappampalayam Easwaramoorthy ;
Cho, Jaehyuk ;
Subramanian, Malliga ;
Naren, Obuli Sai .
FIRE ECOLOGY, 2023, 19 (01)
[29]   An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach [J].
Abdusalomov, Akmalbek Bobomirzaevich ;
Islam, Bappy M. D. Siful ;
Nasimov, Rashid ;
Mukhiddinov, Mukhriddin ;
Whangbo, Taeg Keun .
SENSORS, 2023, 23 (03)
[30]   Forest fire and smoke detection using deep learning-based learning without forgetting [J].
Veerappampalayam Easwaramoorthy Sathishkumar ;
Jaehyuk Cho ;
Malliga Subramanian ;
Obuli Sai Naren .
Fire Ecology, 19