Earticle

초록

영어

Recent advancements in Deep Learning (DL) techniques for fire detection have mitigated various ecological, economic, and environmental impacts. However, single models in existing literature often perform poorly due to their inability to capture relevant features with limited data. In this study, we therefore develop an innovative framework for effective fire detection using teacher-student collaborative knowledge distillation. The framework comprises two main components: the teacher model, which leverages a pretrained InceptionV3, and a customized student model designed to inherit the knowledge from the pretrained InceptionV3 to a pruned student model. The proposed network is evaluated and compared against several competitive techniques using two datasets, MAFire and Yar, with different evaluation metrics, offering higher performance and lower computational cost.

목차

Abstract
I. INTRODUCTION
II. THE PROPOSED METHOD
A. Knowledge Distillation
III. RESULTS AND DISCUSSION
A. Comparison with Various Competitive Techniques
IV. CONCLUSION
ACKNOWLEDGEMENT
REFERENCE

키워드

저자

• Taimoor Khan [ Dept of Computer Engineering, Gachon University ]
• Sae Bom Lee [ Dept of Computer Engineering, Gachon University ]
• NamGyu Jung [ Dept of Computer Engineering, Gachon University ]
• Chang Choi [ Dept of Computer Engineering, Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004