Earticle

초록

한국어

Integrating unmanned aerial vehicles (UAVs) and computer vision techniques has contributed to enhancing the accuracy and speed of monitoring for surveillance and warning systems. This paper presents an application of human detection in a beach-warning-system using drone-captured images and YOLO. Our research focuses on detecting critical objects and anomalies on the beach, such as people or buoys. By leveraging the real-time capabilities of YOLO, our system processes highresolution drone images to swiftly identify and classify objects, enabling rapid responses in emergencies. We conducted evaluations using several methods to validate the model's effectiveness. The results showcase its potential to enhance beach warning systems and quick warning of dangerous situations.

목차

Abstract
I. INTRODUCTION
II. HUMAN DETECTION USING YOLO
A. Object detection using YOLO
B. Advantages of YOLOv8
III. EXPERIMENTAL RESULTS
A. Dataset
B. Results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Nam Anh Bui [ High School of Education Sciences University of Education Hanoi, Vietnam ]
• Ngoc Dung Bui [ Faculty of Information Technology University of Transport and Communications Hanoi, Vietnam ] Corresponding Author
• Nguyen Hanh Bui [ Chu Van An High School Hanoi, Vietnam ]
• Nguyen Hoang Bui [ State University of New York at Buffalo New York, USA ]
• Kien Truc Le [ Department of Electrical Engineering National Chung Cheng University Tai Chung, Taiwan ]
• Quang Tuyen Vu [ Transport Environmental Engineering Center University of Transport and Communications Hanoi, Vietnam ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004