异常检测
重要事件
深度学习
计算机科学
异常(物理)
图像(数学)
人工智能
人工神经网络
机器学习
数据科学
地理
地图学
物理
凝聚态物理
作者
Jiaqi Liu,Guoyang Xie,Jinbao Wang,Shangnian Li,Chengjie Wang,Feng Zheng,Yaochu Jin
标识
DOI:10.1007/s11633-023-1459-z
摘要
Abstract 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 promising setting from industrial manufacturing and review the current IAD approaches under our proposed 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 .
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