A study on the algorithm for fire recognition for automatic forest fire detection: The International Conference on Control, Automation and Systems 2010 (ICCAS 2010)

ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Ho-Woong Choi ◽  
In-Kyu Min ◽  
Eui-Seok Oh ◽  
Dong-Ho Park
Keyword(s):  
Forest Fire ◽  
Fire Detection ◽  
International Conference ◽  
Control Automation ◽  
Fire Recognition ◽  
Forest Fire Detection

Study of routing protocols used in sensor networks for forest fire detection

2020 ◽  
Author(s):  
Jose Guaman'Quiche ◽  
Edwin Guaman-Quinche ◽  
Hernan Torres-Carrion ◽  
Wilman Chamba-Zaragocin ◽  
Franciso Alvarez-Pineda
Keyword(s):  
Sensor Networks ◽  
Forest Fire ◽  
Routing Protocols ◽  
Fire Detection ◽  
Forest Fire Detection

A novel approach of WSN routing protocols comparison for forest fire detection

2018 ◽  
Vol 26 (3) ◽  
pp. 1857-1867 ◽  
Author(s):  
Noureddine Moussa ◽  
Abdelbaki El Belrhiti El Alaoui ◽  
Claude Chaudet
Keyword(s):  
Forest Fire ◽  
Routing Protocols ◽  
Fire Detection ◽  
Novel Approach ◽  
Forest Fire Detection

scholarly journals Forest Fire Detection via Feature Entropy Guided Neural Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 128
Author(s):  
Zhenwei Guan ◽  
Feng Min ◽  
Wei He ◽  
Wenhua Fang ◽  
Tao Lu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Forest Fire ◽  
State Of The Art ◽  
Fire Detection ◽  
High Entropy ◽  
Training Samples ◽  
Feature Information ◽  
Forest Fire Detection ◽  
Entropy Source

Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.

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