Earticle

초록

영어

Forest fire is one of the most dangerous disasters worldwide, due to which its management is a key concern of the research community to prevent social, ecological, and economic damages. Wildfires are extremely catastrophic disasters that lead to the destruction of forests, human assets, reduction of soil fertility and cause global warming. To overcome such kind of losses early fire detection and quick response is the key concern of research community. Therefore, in this paper, we propose a lightweight convolution neural network (CNN) method to efficiently detect the forest fire for unmanned aerial vehicles (UAVs) or drones. For the experimental evaluations, we develop an aerial images dataset from YouTube, movies, and google images. The results of the proposed architecture reveal its good performance in terms of 96% accuracy.

목차

Abstract
1. Introduction
2. Method
3. Results and Discussion
A. Dataset Explanation
B. Results Evaluations
C. Resutls Comparison
D. Visualized Results
4. Conlusion
Acknowledgement
References

키워드

저자

• Hikmat Yar [ Sejong University ]
• Noman Khan [ Sejong University ]
• Fath U Min Ullah [ Sejong University ]
• Mi Young Lee [ Sejong University ]
• Sung Wook Baik [ Sejong University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004