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
- 발행연도
- 2026.6
- 수록면
- 880 - 886 (7page)
- DOI
- 10.5302/J.ICROS.2026.26.0014
이용수
초록· 키워드
In the manufacturing industry, vision inspection technology based on deep learning exhibits excellent performance in detecting unstructured defects, but in the actual process, it has been difficult to secure sufficient learning data owing to the absolute lack of defect data and class imbalance. In this study, we propose a semi-supervised learning system based on the FixMatch algorithm to solve this problem and accurately detect fine defects in the brushless direct current motor stator pin insertion process for automobiles. The proposed system streamlined the preprocessing process by extracting the region of interest using a jig based on fixed coordinates and maximized the generalization performance of the model by combining weak and strong augmentation. The experiment was performed using data collected from the actual manufacturing line, and only a part of the entire learning data was used as label data. According to the experimental findings, the proposed FixMatch model exhibited superior performance compared with that of the supervised learning-based baseline model under the same conditions. In particular, the proposed model achieved a high accuracy rate even in pins with high detection difficulty due to lighting interference and confirmed the robustness of field variables.
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목차
- Abstract
- I. 서론
- II. BLDC 모터 및 검사 시스템 구성
- III. 제안하는 결함 검출 방법
- IV. 실험 결과
- IV. 실험 결과
- REFERENCES
참고문헌
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