논문 상세보기
pp.312-320
Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.
1.1 연구 배경 및 필요성
1.2 연구절차 및 구성
2. 관련 문헌 연구
3. 방법론
3.1 일관성 정규화(Consistency Regularization)
3.2 엔트로피 최소화(Entropy Minimization)
3.3 MixUp
3.4 MixMatch Algorithm [2]
4. 실험 및 결과
4.1 데이터 소개
4.2 실험 내용
4.3 평가 지표
4.4 실험 결과
5. 결론 및 추후연구
Acknowledgement
References
