PUB - Publikationen an der Universität Bielefeld

The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this pa- per, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural net- work architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting from indus- trial manufacturing and review the current IAD approaches under our proposed setting. Moreover, we highlight several opening chal- lenges 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.