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：近年来深度学习的快速发展为工业图像异常检测领域树立了里程碑。本文从神经网络架构、监督程度、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。同时，我们提炼出工业生产中的新场景，并在此框架下审视了当前工业异常检测方法。此外，我们重点探讨了图像异常检测面临的若干开放性挑战，系统分析了不同监督模式下代表性网络架构的优劣。最后，总结研究发现并展望未来研究方向。更多资源请访问 https://github.com/M-3LAB/awesome-industrial-anomaly-detection。