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.
초록·키워드
Image-based coral reef survey technologies have revolutionized the monitoring of coral reefs by o ering a cost-e ective and noninvasive method for collecting data across large spatial scales and extended periods. Among these technologies, underwater videography has emerged as a well-established and reliable tool for remote sensing in coral research. Automatic segmentation of coral images represents a forward-looking and fundamental research area in underwater remote sensing. It aims to address a major challenge that limits traditional in situ underwater coral survey research: the di culty of automatically generating accurate and reproducible high-resolution maps of the underlying coral reef ecosystems. Understanding recent achievements and their relevance to coral ecology monitoring needs is crucial for future planning. This paper presents a literature review on underwater coral image segmentation, focusing on the deep learning implementation pipeline. Furthermore, we introduce a new densely annotated dataset speci cally designed for the semantic segmentation of underwater coral images. We systematically evaluate State-of-the-Art (SOTA) methodologies and novel techniques not previously applied to coral image semantic segmentation using the proposed dataset. We then discuss their feasibility in this context. Our goal for this review is to spark innovative ideas and directions for future research in underwater coral image segmentation and to provide readers with an accessible overview of some of the most signi cant advancements in this eld over the past decade. By accomplishing these objectives, we hope to advance research in underwater coral image segmentation and support the development of e ective monitoring and conservation strategies for coral reef ecosystems.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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