Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning

Yaohua Guo, Lijuan Song, Zirui Ma

A common study area in anomaly identification is industrial images anomaly detection based on texture background. The interference of texture images and the minuteness of texture anomalies are the main reasons why many existing models fail to detect anomalies. We propose a strategy for anomaly detection that combines dictionary learning and normalizing flow based on the aforementioned questions. The two-stage anomaly detection approach already in use is enhanced by our method. In order to improve baseline method, this research add normalizing flow in representation learning and combines deep learning and dictionary learning. Improved algorithms have exceeded 95$\%$ detection accuracy on all MVTec AD texture type data after experimental validation. It shows strong robustness. The baseline method's detection accuracy for the Carpet data was 67.9%. The article was upgraded, raising the detection accuracy to 99.7%.

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