인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보통신학회JICCE Journal of information and communication convergence engineering Journal of information and communication convergence engineering Vol.23 No.1
- 발행연도
- 2025.3
- 수록면
- 8 - 16 (9page)
이용수
초록· 키워드
In this study, a supervised deep learning approach is employed to construct a model that learns the relationship between input data and the corresponding output labels. The objective is to enable the model to generate accurate predictions for unseen data. Furthermore, we propose a method for semantically segmenting and tracking real-time drivable road areas to visually display them. To achieve this objective, the feasibility and performance of real-time applications using you only look once v8(YOLOv8) architecture and mask regional-based convolutional neural network (Mask R-CNN) algorithm for learning are compared. Drivable road area segmentation involves delineating the present drivable zone across several lanes in the direction of vehicle travel, necessitating the detection of the status of the preceding vehicle. The identification rate for area detection is generally high during both day and night under clear conditions. Furthermore, the model derived from the YOLOv8 architecture demonstrated superior performance in visually extracting drivable area segmentation compared with the Mask R-CNN-based model.
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목차
- Abstract
- I. INTRODUCTION
- II. PERTINENT RESEARCH
- III. SEMANTIC SEGMENTATION AND REAL-TIME TRACKING OF DRIVABLE ROAD AREAS
- IV. IMPLEMENTATION RESULTS AND ANALYSIS
- V. CONCLUSION
- REFERENCES
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-151-25-02-092844739
