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：近年来深度学习的迅速发展为工业图像异常检测（IAD）领域树立了里程碑。本文从神经网络架构、监督级别、损失函数、评估指标与数据集等维度，全面综述了基于深度学习的图像异常检测技术。此外，我们从工业制造场景中提炼出新设置，并在该新设置框架下对现有IAD方法进行了评述。进一步地，我们着重指出了图像异常检测领域的若干开放性挑战，同时讨论了不同监督模式下代表性网络架构的优缺点。最后，我们总结了研究发现并指出了未来研究方向。更多资源请访问 https://github.com/M-3LAB/awesome-industrial-anomaly-detection。