Earticle

초록

영어

Unmanned aerial vehicles (UAVs) or drones are versatile innovations that can capture pictures and videos and even collect air or soil samples. Natural disaster drones are especially critical, which help with understanding the damage after a disaster, locating people who need help, distributing resources and preparing for the next event. Computer vision, deep learning (DL), and drones can augment the existing sensors, thereby increasing the accuracy of natural disasters detector, and most importantly, allow people to take precautions, stay safe, and reduce the number of deaths and injuries that happens due to these disasters. Therefore, in this paper we propose a novel lightweight convolutional neural network (CNN) based framework to detect natural disasters including cyclone, flood, earthquake, and wildfire. The proposed CNN model is obtained by fine-tuning the MobileNetV2 that can be deployed on drones. Furthermore, the model is trained and evaluated using a publicly available natural disasters dataset by obtaining 83.4% accuracy. Similarly, the framework has ability to broad cast the notification in alarming situations, which makes our proposed framework a best fit for natural disasters detection in realworld surveillance settings.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. PROPOSED NATURAL DISASTERS DETECTION FRAMEWORK
IV. EXPERIMENTAL RESULTS
A. Dataset
B. System Configuration
C. Resutls and Discussions
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Noman Khan [ Sejong University Seoul, Republic of Korea ]
• Samee Ullah 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 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004