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Deep Industrial Image Anomaly Detection: A Survey
Jiaqi Liu1; Guoyang Xie1,2; Jinbao Wang1; Shangnian Li1; Chengjie Wang3; Feng Zheng1; Yaochu Jin2,4
Source PublicationMachine Intelligence Research

The 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

KeywordImage anomaly detection, defect detection, industrial manufacturing, deep learning, computer vision
Sub direction classification其他
planning direction of the national heavy laboratory其他
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Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
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
Recommended Citation
GB/T 7714
Jiaqi Liu,Guoyang Xie,Jinbao Wang,et al. Deep Industrial Image Anomaly Detection: A Survey[J]. Machine Intelligence Research,2024,21(1):104-135.
APA Jiaqi Liu.,Guoyang Xie.,Jinbao Wang.,Shangnian Li.,Chengjie Wang.,...&Yaochu Jin.(2024).Deep Industrial Image Anomaly Detection: A Survey.Machine Intelligence Research,21(1),104-135.
MLA Jiaqi Liu,et al."Deep Industrial Image Anomaly Detection: A Survey".Machine Intelligence Research 21.1(2024):104-135.
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