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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Veröffentlicht in:	IEEE access Jg. 11; S. 1
Hauptverfasser:	Cui, Yajie, Liu, Zhaoxiang, Lian, Shiguo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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