890 research outputs found
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
In line with the development of Industry 4.0, surface defect
detection/anomaly detection becomes a topical subject in the industry field.
Improving efficiency as well as saving labor costs has steadily become a matter
of great concern in practice, where deep learning-based algorithms perform
better than traditional vision inspection methods in recent years. While
existing deep learning-based algorithms are biased towards supervised learning,
which not only necessitates a huge amount of labeled data and human labor, but
also brings about inefficiency and limitations. In contrast, recent research
shows that unsupervised learning has great potential in tackling the above
disadvantages for visual industrial anomaly detection. In this survey, we
summarize current challenges and provide a thorough overview of recently
proposed unsupervised algorithms for visual industrial anomaly detection
covering five categories, whose innovation points and frameworks are described
in detail. Meanwhile, publicly available datasets for industrial anomaly
detection are introduced. By comparing different classes of methods, the
advantages and disadvantages of anomaly detection algorithms are summarized.
Based on the current research framework, we point out the core issue that
remains to be resolved and provide further improvement directions. Meanwhile,
based on the latest technological trends, we offer insights into future
research directions. It is expected to assist both the research community and
industry in developing a broader and cross-domain perspective
High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis
We propose a novel method for Zero-Shot Anomaly Localization that leverages a
bidirectional mapping derived from the 1-dimensional Wasserstein Distance. The
proposed approach allows pinpointing the anomalous regions in a texture with
increased precision by aggregating the contribution of a pixel to the errors of
all nearby patches. We validate our solution on several datasets and obtain
more than a 40% reduction in error over the previous state of the art on the
MVTec AD dataset in a zero-shot setting
- …