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.
피인용 0
초록·키워드
Instance segmentation is a key technology for precision agriculture tasks such as per-plant growth monitoring and harvest-timing prediction. However, in real smart-farm environments where crops are densely cultivated, instance segmentation poses two practical challenges. First, because pixel-level annotation is highly costly, only a small number of labeled images are typically available. Second, severe leaf overlap between adjacent plants causes boundary ambiguity that degrades segmentation accuracy. To improve segmentation performance, prior studies have explored incorporating structural priors of the target object into the learning process. However, existing methods require redesigned activation functions or separate optimization modules, making them hard to integrate with foundation models and largely limited to single-object semantic segmentation. To address these limitations, we propose ‘radial loss,’ an auxiliary loss function that explicitly captures the radial convex structure of rosette-shaped crops by supervising predicted foreground probabilities along rays cast from each instance's center of mass toward its boundary. We further introduce ‘dynamic search,’ a threshold-triggered sampling mechanism that focuses supervision on boundary regions once the validation mAP exceeds a predefined threshold. Experiments on three lettuce datasets show that, under an extreme 10-shot setting, the proposed method improves mean mAP by 7.7% points over the baseline, demonstrating its effectiveness in label-scarce, densely cultivated environments.
본문·목차
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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