Earticle

현재 위치 Home

Convergence of Internet, Broadcasting and Communication

A study of Strawberry Maturity Classification Using Improved Faster R-CNN

첫 페이지 보기
  • 발행기관
    국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
  • 간행물
    International Journal of Internet, Broadcasting and Communication 바로가기
  • 통권
    Vol.16 No.4 (2024.12)바로가기
  • 페이지
    pp.133-140
  • 저자
    Taewook Kim, Heejun Youn, Seunghyun Lee, Soonchul Kwon
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A459065

※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

원문정보

초록

영어
In strawberry cultivation, maturity classification plays an important role in ensuring the efficiency and quality of harvesting. In this study, we propose an Improved Faster R-CNN model to address these challenges, using MobileNetV3-Large as the backbone network to achieve a lightweight model, and introducing RoI Align to improve the spatial accuracy of the feature map. Experiments are conducted using the KGCV_Strawberry dataset, with precision, recall, F1 score, and mean average precision (mAP) measured for performance evaluation. The experimental results show that the proposed model achieves an average precision of 71.35%, recall of 71.07%, and F1 score of 71.21% across all classes. In particular, the proposed model achieves 63% performance on mAP0.5 and 58% performance on mAP0.5:0.95, which is comparable to existing ResNet-based models while achieving faster inference speed. The proposed model achieves a processing speed of 27.6543 ms, which is about 2 ms faster than existing ResNet-based models. This indicates that the goal of creating a lightweight model with improved image processing capability was achieved with minimal performance degradation. This research is expected to contribute to the development of automated strawberry cultivation systems in greenhouse environments and has the potential to be applied to various agricultural environments in the future.

목차

Abstract
1. Introduction
2. Background Theory
2.1 Faster R-CNN
2.2 Related Work
3. Experiment and Methods
3.1 Image Acquisition
3.2 Experiments Environment
3.3 Proposed Method
3.4 Evaluation Metrics
4. Result
4.1 Strawberry Classification Maturity Stage
5. Conclusion
6. Acknowledgement
References

키워드

Convolutional Neural Network (CNN) Faster R-CNN Image Classification RoI Align Strawberry Maturity

저자

  • Taewook Kim [ M.S, Department of Smart System, Kwangwoon University, Seoul, South Korea ]
  • Heejun Youn [ M.S, Department of Plasma Bio Display, Kwangwoon University, Seoul, South Korea ]
  • Seunghyun Lee [ Professor, Ingenium College Liberal Arts, Kwangwoon University, Seoul, South Korea ]
  • Soonchul Kwon [ Associate professor, Graduate School of Smart Convergence, Kwangwoon University, Seoul, South 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

이 권호 내 다른 논문 / International Journal of Internet, Broadcasting and Communication Vol.16 No.4

    피인용수 : 0건 (자료제공 : 네이버학술정보)

    함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.

      출처 : 네이버학술정보

        0개의 논문이 장바구니에 담겼습니다.

        페이지 저장
        소속기관 조회
        이용자님의 소속기관(단체)이 서비스에 가입되어 있는지 확인해 보십시오.
        기관회원에 소속되어 있는 이용자는 원문을 무료로 이용할 수 있습니다.