Deep Industrial Image Anomaly Detection: A Survey
This is an incremental survey paper that organizes existing knowledge for researchers and practitioners in industrial quality control.
This paper provides a comprehensive survey of deep learning techniques for industrial image anomaly detection, reviewing methods from multiple perspectives and proposing a new setting for evaluating approaches in manufacturing contexts.
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 new setting from industrial manufacturing and review the current IAD approaches under our proposed our new 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.