Earticle

초록

영어

Accurate detection of small targets in aerial images is crucial but challenging due to the limited computational resources of UAVs. This paper presents an efficient approach based on YOLO-V5S for detecting and classifying distant vehicles in aerial scenes. Extensive ablation study is conducted to find the optimal YOLO architecture. The proposed method is efficient and effective, making it applicable for real-time deployment. A dataset of 1000 annotated images are developed to validate the proposed method's effectiveness. The proposed network outperforms existing state-of-the-art methods in accuracy, speed, and resource efficiency, making it a promising solution for aerial vision-based applications.

목차

Abstract
1. Introduction
2. The proposed method
2.1. Data acquisition and preprocessing
2.2. Model architecture
3. Experiment result
3.1. Experimental setting
3.2. Experimental results
4. Conclusions
Acknowledgement
References

키워드

저자

• Habib Khan [ Sejong University Seoul, Republic of Korea ]
• Zulfiqar Ahmad Khan [ Sejong University Seoul, Republic of Korea ]
• Waseem Ullah [ Sejong University Seoul, Republic of Korea ]
• Min Jee Kim [ 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

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004