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오토 인코더와 단일클래스 SVM을 적용한 결함 검출 연구
- Alternative Title
- A Defect Detection Method Using Auto-Encoder and One-Class SVM
- Author(s)
- 정상교
- Alternative Author(s)
- Jeong Sang Kyo
- Advisor
- 구형일
- Department
- 일반대학원 전자공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2016-08
- Language
- kor
- Alternative Abstract
- In this paper, we propose a new defect detection method using a deep autoencoder and one-class support vector machine. The proposed method extracts patches in insert images and classi_x000C_es each patch into normal and defect one. However, the appearance of defects varies from case to case and it is very di_x000E_cult to collect all possible defect patch images, which hinders the use of conventional binary classi_x000C_cation methods. Therefore, we develop a novel method that only requires normal patches. To be precise, the method uses a deep auto-encoder as a feature extractor, which is trained with only normal patches, and one-class SVM is adopted to determine the decision boundary of normal cases. Experimental results show that the proposed method works robustly for light changes and improves the classi_x000C_cation performance compared with conventional methods.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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