소량 데이터 딥러닝 기반 강판 표면 결함 검출 시스템 개발Development of a Steel Plate Surface Defect Detection System Based on Small Data Deep Learning
- Other Titles
- Development of a Steel Plate Surface Defect Detection System Based on Small Data Deep Learning
- Authors
- 게이뷸라예프 압둘라지즈; 이나현; 이기환; 김태형
- Issue Date
- Jun-2022
- Publisher
- 대한임베디드공학회
- Keywords
- Steel plate; Defect detection; Deep learning; Data augmentation; Semi-supervised learning
- Citation
- 대한임베디드공학회논문지, v.17, no.3, pp 129 - 138
- Pages
- 10
- Journal Title
- 대한임베디드공학회논문지
- Volume
- 17
- Number
- 3
- Start Page
- 129
- End Page
- 138
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28389
- DOI
- 10.14372/IEMEK.2022.17.3.129
- ISSN
- 1975-5066
- Abstract
- Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.
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