论文标题
通过融合正常流量和词典学习来融合自我监督的纹理图像异常检测
Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning
论文作者
论文摘要
异常鉴定中的一个常见研究区域是基于纹理背景的工业图像异常检测。 The interference of texture images and the minuteness of texture anomalies are the main reasons why many existing models fail to detect anomalies.我们提出了一种异常检测策略，该策略将字典学习和基于上述问题的归一流流程结合在一起。我们的方法增强了已经使用的两阶段异常检测方法。为了改善基线方法，这项研究增加了表示学习中的正常流程，并结合了深度学习和词典学习。在实验验证后，所有MVTEC AD纹理类型数据的改进算法超过了95 $ \％$检测准确性。它显示出强大的鲁棒性。地毯数据的基线方法的检测准确性为67.9％。该文章已升级，将检测准确性提高到99.7％。
A common study area in anomaly identification is industrial images anomaly detection based on texture background. The interference of texture images and the minuteness of texture anomalies are the main reasons why many existing models fail to detect anomalies. We propose a strategy for anomaly detection that combines dictionary learning and normalizing flow based on the aforementioned questions. The two-stage anomaly detection approach already in use is enhanced by our method. In order to improve baseline method, this research add normalizing flow in representation learning and combines deep learning and dictionary learning. Improved algorithms have exceeded 95$\%$ 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%.
