A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes topical subject in industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than t...

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Vydáno v:IEEE access Ročník 11; s. 1
Hlavní autoři: Cui, Yajie, Liu, Zhaoxiang, Lian, Shiguo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Anomalies
Anomaly detection
Deep learning
Filter banks
Gabor filters
Industrial Anomaly detection
Industrial development
Industry 4.0
Inspection
Labor
Machine learning
Production
Supervised learning
Surface defects
Surveys
Unsupervised learning
Visualization
ISSN:2169-3536, 2169-3536
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Shrnutí:In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes topical subject in industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3282993