Earticle

초록

영어

The technologies underlying fire and smoke detection systems play crucial roles in ensuring that these systems deliver optimal performance in modern. In fact, fire can cause significant damage to lives and properties. In majority of cities, camera-monitoring systems have been already installed, to take advantage of availability of these kinds of systems encourage us to develop a cost-effective vision detection methods. However, this is a complex vision task by the reason of perspective deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a method-based on deep learning that uses a convolutional neural network, which employs dilated convolutions. We evaluated our method, by training and testing it on our custom-built dataset. Consisting of a collection of fire and smoke images that we collected and labeled manually. The performances of methods proposed in previous studies were compared with those of well-known state-of-the-art architectures; our experimental results indicate that the classification performance and complexity of our method was superior to those of previous methods. In addition, our method is designed to be well generalized for unseen data, that it offers effective generalization and also reduces the number of false alarms.

목차

Abstract
1. Introduction
2. Related Work
2.1 Computer Vision Approaches For Fire and Smoke Detection
2.2 Deep Learning Appraoches For Fire and Smoke Detection
3. Dataset
4. Proposed Architecture
4.1 Brief Summary of Well-known Network Architectures
4.2 Dilated Convolution
4.3 Proposed Network Architecture
5. Experiments and Discussions
5.1. Investigating the Optimum Method for Fire and Smoke Detection
5.2 Comparison of Our Network Model with Well-Known Architectures by Conducting Experiments on Our Dataset
6. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References

키워드

저자

• 약요쿠자 발리쿠자에브 [ Yakhyokhuja Valikhujaev | 가천대 컴퓨터공학과 ]
• 마크무드 파즈리딘 [ MakhmudovFazliddin | 가천대 컴퓨터공학과 ]
• 묵시모바 샥노자 [ MuksimovaShakhnoza | 가천대 컴퓨터공학과 ]
• 조영임 [ YoungImCho | 가천대 컴퓨터공학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국화재감식학회 학회지 [Magazine of Fire Investigation Socity of Korea]

• 간기

  계간

• pISSN

  2092-531X

• 수록기간

  2009~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 539 DDC 628