Open AccessImage Defect Detection and Root Cause Analysis
Author(s) -
Tiancheng Liu,
Hao-Ran Yu,
Xu Han,
Haisong Gu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1487/1/012031
Subject(s) - root cause , artificial intelligence , deep learning , computer science , economic shortage , root (linguistics) , root cause analysis , image (mathematics) , pattern recognition (psychology) , machine learning , computer vision , engineering , reliability engineering , linguistics , philosophy , government (linguistics)
Artificial Intelligence has played an increasingly important role in visual defect detection in recent years, while there are many challenges using deep learning for this application, such as the shortage of data, lack of knowledge of root cause of defects. In this paper, we combine deep learning with traditional AI methods, not only to solve unshaded defect detection but also find root causes of detected defects. First, we propose a taxonomy method called Dataonomy SM to extend a meta defect dataset with a small number of samples and a deep learning method to detect the image defects. For detected defect images, we use a generalized multi-image matting algorithm to extract common defects automatically. We apply this technology to identify defects that stem from systematic errors in a product line and later extended its use to watermark processing. Experimental results have shown great capability and versatility of our proposed methods.