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
In certain industrial environments, it is difficult to collect many real images for specific target objects. To address this limitation and automate the classification process, an image classification model was developed using synthetic images generated through 3D modeling. In this study, Lego blocks were modeled in 3D CAD and rendered to produce photorealistic images. The rendered images were augmented using techniques such as flip and brightness adjustment to expand the dataset. Two convolutional neural network (CNN) models, ResNet18 and GhostNet, were trained and compared. A total of 8,960 augmented synthetic images and 1,000 real images were used for training and evaluation. As a result, ResNet18 showed a higher average classification accuracy than GhostNet. Consequently, ResNet18 was selected as the final classification model. In experiments comparing rendering styles, synthetic images with realistic shadows and materials achieved 95.6% accuracy, while less realistic images achieved only 86.0%. These findings suggest that the more visually realistic the synthetic images are, the more positively they affect the classification performance. Additionally, synthetic images with Gaussian noise achieved a classification accuracy of 99.2%, implying that incorporating noise characteristics commonly found in real environments can further enhance model performance.
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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UCI(KEPA) : I410-151-26-02-094792984
