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Evaluation of Autonomous Driving System Using Deep Learning-based Image Processing

첫 페이지 보기
  • 발행기관
    한국산림공학회 바로가기
  • 간행물
    한국산림공학회 학술대회 바로가기
  • 통권
    International Conference of KSFE-FETEC 2025 (2025.06)바로가기
  • 페이지
    pp.45-45
  • 저자
    Hyeon-Seung Lee, Gyun-Hyung Kim, Hong-Sik Ju, Ho-Seong Mun, Jae-Heun Oh
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A468592

원문정보

초록

영어
In this study, we propose a deep learning-based forest road recognition technology and integrate it into an autonomous steering and speed control system for a forestry forwarder. We trained a YOLOv8 segmentation model on 640×480 pixel images collected from straight, curved, and occluded sections of forestry roads, achieving a precision of 0.997, a recall of 0.994, an F1 score of 0.995, and an average precision of 0.994. We developed a centerline extraction algorithm that calculates the lateral offset between the vehicle center and the detected road centerline using the segmented mask. This offset is then input into an electronic steering lever control system, which replaces the existing mechanical steering lever to enable rapid response. In field experiments conducted on actual forest roads, the maximum average path tracking error was 0.534 m over three trials, confirming that the vision-based approach can reliably guide the forwarder under various lighting conditions, shadows, and roadside structures. This method is expected to overcome the limitations of GNSS/INS-based autonomous driving systems, as it operates solely with cameras and single-board hardware. The research results suggest that deep learning-based image processing provides a solid foundation for fully autonomous forestry operations and has the potential to improve the productivity and safety of timber transport operations.

키워드

forestry machines; forwarder; autonomous driving; image processing; YOLOv8.

저자

  • Hyeon-Seung Lee [ Forest Technology and Management Research Center, National Institute of Forest Science ]
  • Gyun-Hyung Kim [ Forest Technology and Management Research Center, National Institute of Forest Science ]
  • Hong-Sik Ju [ Forest Technology and Management Research Center, National Institute of Forest Science ]
  • Ho-Seong Mun [ Forest Technology and Management Research Center, National Institute of Forest Science ]
  • Jae-Heun Oh [ Forest Technology and Management Research Center, National Institute of Forest Science ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국산림공학회 [Korean Society of Forest Engineering]
  • 설립연도
    2003
  • 분야
    농수해양>임학
  • 소개
    사방, 임도, 벌출, 산림환경보전 및 훼손지 복원 등에 관한 연구 및 기술개발과 회원 상호간의 유기적인 칙목을 도모하여 임학 및 임업의 발전과 산림자원의 보전 및 관련 기술개발에 기여함을 목적으로함.

간행물

  • 간행물명
    한국산림공학회 학술대회
  • 간기
    연간
  • 수록기간
    2023~2025
  • 십진분류
    KDC 526 DDC 634

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