CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
Deep Industrial Image Anomaly Detection: A Survey
Jiaqi Liu1
Source PublicationMachine Intelligence Research
ISSN2731-538X
2024
Volume21Issue:1Pages:104-135
AbstractThe recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting from industrial manufacturing and review the current IAD approaches under our proposed setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.
KeywordImage anomaly detection, defect detection, industrial manufacturing, deep learning, computer vision
DOI10.1007/s11633-023-1459-z
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/54578
Collection学术期刊_Machine Intelligence Research
Affiliation1.Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen 518055, China
2.NICE Group, University of Surrey, Guildford GU2 7YX, UK
3.Youtu Lab, Tencent, Shanghai 200233, China
4.NICE Group, Bielefeld University, Bielefeld 33619, Germany
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GB/T 7714
Jiaqi Liu. Deep Industrial Image Anomaly Detection: A Survey[J]. Machine Intelligence Research,2024,21(1):104-135.
APA Jiaqi Liu.(2024).Deep Industrial Image Anomaly Detection: A Survey.Machine Intelligence Research,21(1),104-135.
MLA Jiaqi Liu."Deep Industrial Image Anomaly Detection: A Survey".Machine Intelligence Research 21.1(2024):104-135.
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