DRONE IMAGERY FOREST FIRE DETECTION AND CLASSIFICATION USING MODIFIED DEEP LEARNING MODEL

被引:0
作者
Mashraqi, Aisha M. [1 ]
Asiri, Yousef [1 ]
Algarni, Abeer D. [2 ]
Abu-zinadah, Hanaa [3 ]
机构
[1] Najran Univ, Coll Comp Sci & Informat Syst, Dept Comp Sci, Najran, Saudi Arabia
[2] Princess Nourah bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Technol, Riyadh, Saudi Arabia
[3] Univ Jeddah, Coll Sci, Dept Stat, Jeddah, Saudi Arabia
来源
THERMAL SCIENCE | 2022年 / 26卷
关键词
forest fire; computer vision; drone imagery; deep learning; metaheuristics; machine learning;
D O I
暂无
中图分类号
O414.1 [热力学];
学科分类号
摘要
With the progression of information technologies, unmanned aerial vehicles (UAV) or drones are more significant in remote monitoring the environment. One main application of UAV technology relevant to nature monitoring is monitoring wild animals. Among several natural disasters, Wildfires are one of the deadliest and cause damage to millions of hectares of forest lands or resources which threatens the lives of animals and people. Drones present novel features and con-venience which include rapid deployment, adjustable and wider viewpoints, less human intervention, and high maneuverability. With the effective enforcement of deep learning in many applications, it is used in the domain of forest fire recogni-tion for enhancing the accuracy of forest fire detection through extraction of deep semantic features from images. This article concentrates on the design of the drone imagery forest fire detection and classification using modified deep lear-ning (DIFFDC-MDL) model. The presented DIFFDC-MDL model aims in the detection and classification of forest fire in drone imagery. To accomplish this, the presented DIFFDC-MDL model designs a modified MobileNet-v2 model to generate feature vectors. For forest fire classification, a simple recurrent unit model is applied in this study. In order to further improve the classification out-comes, shuffled frog leap algorithm is used. The simulation outcome analysis of the DIFFDC-MDL system was tested utilizing a database comprising fire and non-fire samples. The extensive comparison study referred that the improvements of the DIFFDC-MDL system over other recent algorithms.
引用
收藏
页码:S411 / S423
页数:13
相关论文
共 50 条
  • [41] Ransomware Detection and Classification Using Machine Learning and Deep Learning
    Ouerdi, Noura
    Mejjout, Brahim
    Laaroussi, Khadija
    Kasmi, Mohammed Amine
    ADVANCES IN SMART MEDICAL, IOT & ARTIFICIAL INTELLIGENCE, VOL 1, ICSMAI 2024, 2024, 11 : 194 - 201
  • [42] Forest Fire Spread Prediction using Deep Learning
    Khennou, Fadoua
    Ghaoui, Jade
    Akhloufi, Moulay A.
    GEOSPATIAL INFORMATICS XI, 2021, 11733
  • [43] Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning
    Benzekri, Wiame
    El Moussati, Ali
    Moussaoui, Omar
    Berrajaa, Mohammed
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (05) : 496 - 503
  • [44] A real-time forest fire and smoke detection system using deep learning
    Mohammed, Raghad K.
    INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 2022, 13 (01): : 2053 - 2063
  • [45] Semantic Segmentation of Drone Imagery Using Deep Learning for Seagrass Habitat Monitoring
    Jeon, Eui-Ik
    Kim, Seong-Hak
    Kim, Byoung-Sub
    Park, Kyung-Hyun
    Choi, Ock-In
    KOREAN JOURNAL OF REMOTE SENSING, 2020, 36 (02) : 199 - 215
  • [46] DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning Networks
    Al Dawasari, Hassan J.
    Bilal, Muhammad
    Moinuddin, Muhammad
    Arshad, Kamran
    Assaleh, Khaled
    SENSORS, 2023, 23 (21)
  • [47] Human Object Detection in Forest with Deep Learning based on Drone's Vision
    Yong, Suet-Peng
    Yeong, Yoon-Chow
    2018 4TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCES (ICCOINS), 2018,
  • [48] Deep Learning Based Malicious Drone Detection Using Acoustic and Image Data
    Kim, Juann
    Lee, Dongwhan
    Kim, Youngseo
    Shin, Heeyeon
    Heo, Yeeun
    Wang, Yaqin
    Matson, Eric T.
    2022 SIXTH IEEE INTERNATIONAL CONFERENCE ON ROBOTIC COMPUTING, IRC, 2022, : 91 - 92
  • [49] FireWarn: Fire Hazards Detection Using Deep Learning Models
    Hogan, Isaac
    Qiao, Donghao
    Luo, Ruikang
    Moattari, Mojtaba
    Carthy, Austin
    Zulkernine, Farhana
    Rivest, Francois
    Breton, Melanie
    2021 IEEE THIRD INTERNATIONAL CONFERENCE ON COGNITIVE MACHINE INTELLIGENCE (COGMI 2021), 2021, : 1 - 10
  • [50] Forest Vegetation Detection Using Deep Learning Object Detection Models
    Mendes, Paulo A. S.
    Coimbra, Antonio Paulo
    de Almeida, Anibal T.
    FORESTS, 2023, 14 (09):