Deep Industrial Image Anomaly Detection: A Survey

Liu，Jiaqi1; Xie，Guoyang1,2; Wang，Jinbao1; Li，Shangnian1; Wang，Chengjie3; Zheng，Feng1; Jin，Yaochu2,4

2024-02-01

10.1007/s11633-023-1459-z

Machine Intelligence Research   影响因子和分区

2731-538X

2731-5398

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 https://github.com/M-3LAB/awesome-industrial-anomaly-detection .

computer vision deep learning defect detection Image anomaly detection industrial manufacturing

ESCI ; EI

英语

第一 ; 通讯

2-s2.0-85182489543

Scopus

1.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
2.NICE Group,University of Surrey,Guildford,GU2 7YX,United Kingdom
3.Youtu Lab,Tencent,Shanghai,200233,China
4.NICE Group,Bielefeld University,Bielefeld,33619,Germany

Liu，Jiaqi,Xie，Guoyang,Wang，Jinbao,et al. Deep Industrial Image Anomaly Detection: A Survey[J]. Machine Intelligence Research,2024,21(1):104-135.

Liu，Jiaqi.,Xie，Guoyang.,Wang，Jinbao.,Li，Shangnian.,Wang，Chengjie.,...&Jin，Yaochu.(2024).Deep Industrial Image Anomaly Detection: A Survey.Machine Intelligence Research,21(1),104-135.

Liu，Jiaqi,et al."Deep Industrial Image Anomaly Detection: A Survey".Machine Intelligence Research 21.1(2024):104-135.