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A Study on Image Labeling Technique for Deep-Learning-Based Multinational Tanks Detection Model

첫 페이지 보기
  • 발행기관
    국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
  • 간행물
    International Journal of Internet, Broadcasting and Communication 바로가기
  • 통권
    Vol.14 No.4 (2022.11)바로가기
  • 페이지
    pp.58-63
  • 저자
    Taehoon Kim, Dongkyun Lim
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A421032

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원문정보

초록

영어
Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

목차

Abstract
1. Introduction
2. Related Theory and Prior Research
2.1 Convolutional Neural Network (CNN)
2.2 You Only Look Once (YOLO) Network
2.3 Prior Research
3. Experiment and Result
3.1 Data Collection and Augmentation
3.2 Data Labeling according to Tank Features
3.3 Tank Identification Model Generation through YOLOv5 Network
3.4 Model Operation Results and Analysis
4. Conclusion
References

키워드

Convolutional Neural Network (CNN) Computer Vision Deep Learning YOLO Network

저자

  • Taehoon Kim [ Master’s Student, School of Industrial and Systems Engineering, Georgia Institute of Technology, USA ]
  • Dongkyun Lim [ Professor, Department of Applied Software Engineering, Hanyang Cyber University, Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
  • 설립연도
    2000
  • 분야
    공학>전자/정보통신공학
  • 소개
    인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.

간행물

  • 간행물명
    International Journal of Internet, Broadcasting and Communication
  • 간기
    계간
  • pISSN
    2288-4920
  • eISSN
    2288-4939
  • 수록기간
    2009~2025
  • 십진분류
    KDC 326 DDC 380

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