An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach

被引:72
作者
Abdusalomov, Akmalbek Bobomirzaevich [1 ]
Islam, Bappy M. D. Siful [1 ]
Nasimov, Rashid [2 ]
Mukhiddinov, Mukhriddin [2 ]
Whangbo, Taeg Keun [1 ]
机构
[1] Gachon Univ, Dept Comp Engn, Seongnam Si 461701, Gyeonggi Do, South Korea
[2] Tashkent State Univ Econ, Dept Artificial Intelligence, Tashkent 100066, Uzbekistan
关键词
forest fire; fire detection; Detectron2; deep learning; fire image dataset; SEGMENTATION;
D O I
10.3390/s23031512
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires. Fast detection with high accuracy is the key to controlling this unexpected event. To address this, we proposed an improved forest fire detection method to classify fires based on a new version of the Detectron2 platform (a ground-up rewrite of the Detectron library) using deep learning approaches. Furthermore, a custom dataset was created and labeled for the training model, and it achieved higher precision than the other models. This robust result was achieved by improving the Detectron2 model in various experimental scenarios with a custom dataset and 5200 images. The proposed model can detect small fires over long distances during the day and night. The advantage of using the Detectron2 algorithm is its long-distance detection of the object of interest. The experimental results proved that the proposed forest fire detection method successfully detected fires with an improved precision of 99.3%.
引用
收藏
页数:19
相关论文
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