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
Object detection, a key area in computer vision, involves locating and classifying objects within images. The YOLO model, which combines candidate region selection, coordinate prediction, and class inference into a single stage, has set a standard for this field. Since its introduction, numerous models have been developed, with applications spanning both academia and industry. Advances in computer vision AI, sensor, and computing technologies have been foundational for autonomous driving and driver assistance systems. Specifically, traffic sign recognition systems using object detection algorithms enhance driving safety. Annotating data for these models is resource-intensive, so many rely on benchmark datasets like MS COCO. However, this dataset has limited images featuring small objects, leading to a gap in research on models capable of detecting significantly smaller objects. Our study addresses this by developing an object detection model for distant road signs as small objects, using a specialized dataset with 2,800 x 2,100 resolution images containing objects smaller than 200 x 200 pixels. We empirically evaluated various models, including State-of-the-art YOLO version and Transformer-based approaches, under different training conditions. The best-performing model for small object detection, balancing accuracy and inference speed, was the pretrained YOLO v6m, utilizing transfer learning with COCO2017 weights without self-distillation. This research contributes to small object detection technology, offering insights for developing new AI algorithms and benefiting both academic and industrial sectors.
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.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-25-02-092320341
