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

• URL: http://arxiv.org/abs/2209.07005v2
• Date: Mon, 19 Sep 2022 02:17:38 GMT
• Title: Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning
• Authors: Yaohua Guo, Lijuan Song, Zirui Ma
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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