期刊
SENSORS
卷 20, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/s20051459
关键词
defect detection; classification; deep learning; industry 4.0; survey
资金
- Tuscany Region by CENTAURO project under the FAR-FAS call for proposals [CUP D88C15000210008]
This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.
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