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
- 2021.8
- 수록면
- 626 - 632 (7page)
- DOI
- 10.5302/J.ICROS.2021.20.0164
이용수
이 논문의 연구 히스토리 (3)
2021
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초록· 키워드
오류제보하기
Compact camera modules are used in many small electronic devices. Defect detection is essential because the cover protecting the module is exposed to the environment outside. It also defects classification to provide feedback to the production process and prevent further defects from occurring. Since the size of the defect is small compared to the size of the module, we adopted the method of extracting the inspection patch. To reduce the inspection time, we employed the compact camera module defect classification system using quadtree decomposition. The module area was detected using template matching, and the unnecessary background was deleted through bit operation. By binarizing the detected module areas, defect candidates were found through quadtree decomposition. The defects were then classified through deep learning. Compared to the previously proposed patch-based method, the inspection time could be reduced.
목차
Abstract
I. 서론
II. CCM 덮개 결함
III. CCM 덮개 결함 분류 시스템
IV. 실험 결과
V. 결론
REFERENCES
참고문헌 (20)
참고문헌 신청
M. H. Ha and T. H. Park, “CCM defect classification system based on machine learning for smart factory,” Journal of the Korean Institute of Industrial Engineers, vol. 46, no. 1, pp. 64-70, Feb. 2020.
Y. G. Kim and T. H. Park, “SMT defect classification by CNN and input image transform,” Journal of Institute of Control, Robotics and Systems(in Korean), vol. 25, no. 3, Mar. pp. 260-267, 2019.
K. H. Choi and J. E. Ha, “Defect detection of Solar Panel using CNN,” Journal of Institute of Control, Robotics and Systems(in Korean),vol. 26, no. 12, pp. 1093-1098, Dec. 2020.
M. K. Lee and K. S. Seo, “Comparison of region-based CNN methods for defects detection on metal surface,” The Transactions of the Korean Institute of Electrical Engineers, vol. 67, no. 1, pp. 865-870, 2018.
I. Konovalenko, P. Maruschak, J. Brezinova, J. Vinas, and J. Brezina, “Steel surface defect classification using deep residual neural network,” Metals 2020, vol. 10, no. 6, pp. 846-860, Jun. 2020.
Y. G. Kim and T. H. Park, “SMT defect classification by CNN and input image transform,” Journal of Institute of Control, Robotics and Systems(in Korean), vol. 25, no. 3, Mar. pp. 260-267, 2019.
K. H. Choi and J. E. Ha, “Defect detection of Solar Panel using CNN,” Journal of Institute of Control, Robotics and Systems(in Korean),vol. 26, no. 12, pp. 1093-1098, Dec. 2020.
M. K. Lee and K. S. Seo, “Comparison of region-based CNN methods for defects detection on metal surface,” The Transactions of the Korean Institute of Electrical Engineers, vol. 67, no. 1, pp. 865-870, 2018.
I. Konovalenko, P. Maruschak, J. Brezinova, J. Vinas, and J. Brezina, “Steel surface defect classification using deep residual neural network,” Metals 2020, vol. 10, no. 6, pp. 846-860, Jun. 2020.
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UCI(KEPA) : I410-ECN-0101-2021-003-001917654