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.

翻译：最近深层学习的迅速发展在工业形象异常探测(IAD)中树立了一个里程碑。在本文件中,我们从神经网络结构、监督水平、损失功能、计量和数据集的角度,全面审查了深层次基于学习的图像异常现象探测技术。此外,我们从工业制造中提取新的环境,并根据我们提出的新环境审查目前IAD的方法。此外,我们强调在发现图像异常方面所面临的一些挑战。我们讨论了不同监督下具有代表性的网络结构的优点和缺点。最后,我们总结了研究结果并指明了未来的研究方向。在https://github.com/M-3LAB/aweome-index-anomaly-detaction中可以找到更多的资源。