TY - GEN
T1 - Early Forest Fire Region Segmentation Based on Deep Learning
AU - Wang, Guangyi
AU - Zhang, Youmin
AU - Qu, Yaohong
AU - Chen, Yanhong
AU - Maqsood, Hamid
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - As the forest fire can bring about great property loss and ecological disaster, artificial intelligence-based forest fire monitoring system has gained popularity in recent years to enable the fire alarm quickly and accurately. In this paper, considering that the fire area is very small and hard to be detected using traditional method for detection early forest fire, we propose a novel forest fire monitoring framework based on convolutional neutral networks. In order to validate that the proposed framework can improve effectiveness and accuracy of detecting the early forest fires, many groups of fire detection experiments using a self-generated forest fire dataset and two real forest fire monitor videos are conducted. The experiment results demonstrate its capability to work in various challenging fire and illumination conditions presented in the study, and show that the framework can effectively detect the early forest fire.
AB - As the forest fire can bring about great property loss and ecological disaster, artificial intelligence-based forest fire monitoring system has gained popularity in recent years to enable the fire alarm quickly and accurately. In this paper, considering that the fire area is very small and hard to be detected using traditional method for detection early forest fire, we propose a novel forest fire monitoring framework based on convolutional neutral networks. In order to validate that the proposed framework can improve effectiveness and accuracy of detecting the early forest fires, many groups of fire detection experiments using a self-generated forest fire dataset and two real forest fire monitor videos are conducted. The experiment results demonstrate its capability to work in various challenging fire and illumination conditions presented in the study, and show that the framework can effectively detect the early forest fire.
KW - Artificial Intelligence
KW - Deep Learning
KW - Forest Fire
KW - Semantic Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85073096752&partnerID=8YFLogxK
U2 - 10.1109/CCDC.2019.8833125
DO - 10.1109/CCDC.2019.8833125
M3 - 会议稿件
AN - SCOPUS:85073096752
T3 - Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019
SP - 6237
EP - 6241
BT - Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st Chinese Control and Decision Conference, CCDC 2019
Y2 - 3 June 2019 through 5 June 2019
ER -