Earticle

초록

영어

Nowadays, due to natural disasters the world is facing huge challenges such as economical, climatic, and losses a lot of precious human life. The traditional emergency response and rescue teams are physically visit different affected areas for inspection and save human lives. In this manual monitoring system created various problems such as human resources, time-consuming, and in real-time unable to accurately analyze the nature of the disaster. Therefore, there is an urgent need for an automatic real-time system to intelligently identified different disaster scenes and analyze the affected areas for quick response. Therefore, in this paper, an Unmanned Aerial Vehicles (UAVs) inspired framework is proposed for disaster scenes classification using a lightweight Convolution Neural Network (CNN). To validate the strength of the proposed framework a comparative analysis is conducted to show its superiority against different state-of-the-art models in terms of computational complexity and performance.

목차

Abstract
I. INTRODUCTION
II. THE PROPOSED SYSTEM
A. Preprocessing phase
B. Proposed lightweight CNN architecture
III. EXPERIMENTAL RESULTS
A. Dataset
B. Result and discussion
IV. CONCLUSIONS AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

저자

• Altaf Hussain [ Sejong University Seoul, Republic of Korea ]
• Samee Ullah Khan [ Sejong University Seoul, Repulic of Korea ]
• Fath U Min Ullah [ Sejong University Seoul, Republic of Korea ]
• Zulfiqar Ahmad Khan [ Sejong University Seoul, Republic of Korea ]
• Mi Young Lee [ Sejong University Seoul, Republic of Korea ]
• Sung Wook Baik [ Sejong University Seoul, Republic of Korea ] Corresponding Author

참고문헌