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（IAD）奠定了里程碑。本文从神经网络架构、监督程度、损失函数、评估指标和数据集等多个维度，对基于深度学习的图像异常检测技术进行了全面综述。此外，我们从工业制造场景中提炼出新的设定框架，并基于该框架对现有IAD方法进行了系统梳理。同时，我们重点指出了图像异常检测领域面临的若干开放性挑战。在不同监督条件下，本文讨论了代表性网络架构的优势与局限。最后，我们总结了研究成果并展望了未来研究方向。更多资源可访问 https://github.com/M-3LAB/awesome-industrial-anomaly-detection。