![]() ![]() Moreover, because those autoencoder-based methods have strong reconstruction ability, anomalies may also be reconstructed with high fidelity, which will degrade the defect detection performance. Existing background reconstruction-based anomaly detection methods, such as autoencoders, and generative adversarial networks (GANs), cannot guarantee that images with large abnormal areas will be reconstructed into normal images. Therefore, the regions with significant differences between the reconstruction image and test image are considered as abnormal regions. When a test image with anomaly areas is input into the network, the anomaly areas cannot be reconstructed well. They cannot reconstruct image patterns that have not been seen previously because the networks are trained by normal images only. Background reconstruction-based methods obtain the anomaly locations according to the differences between the input image and the corresponding reconstructed image. Unsupervised anomaly detection methods can be roughly classified into two categories: background reconstruction-based methods, ,, and feature-based methods,. But there is improvement room for the detection accuracy of the unsupervised learning methods. Unsupervised learning methods are preferred, as they do not require a large amount of defective data. Thus, data-driven-based supervised learning is difficult to be applied in the real production process. Finally, the differences between normal and abnormal images are often subtle due to the small defect areas in high-resolution images. It is very challenging to build a dataset consisting of all abnormal types. ![]() Second, in the manufacturing process, the types of anomalies or defects vary randomly. It is difficult to collect a large number of unqualified product samples. First, the industrial production process has been undergone continuous optimizations in order to reduce the defective rate of products as much as possible. In real industrial manufacturing processes, anomaly detection tasks encounter unique challenges. Compared with the state-of-the-art methods, our proposed method achieves competitive improvement in anomaly detection and localization on the MVTec AD dataset, the two KolektorSDD datasets, and the mSTC dataset.Īnomaly detection and localization based on given normal images show vital potentials in various practical applications, such as intelligent security, ,, , industrial product quality control, , and medical diagnosis, , etc. Finally, a weighted multiscale anomaly map matching method is proposed to detect and locate the anomaly regions of test images. The feature fitting and anomaly scores of the anchor images in the graph vertices are calculated next. Specifically, we build a graph model based on the K nearest neighbors of an anchor image. In this paper, a graph model-based multiscale feature fitting method is proposed for unsupervised anomaly detection. In addition, existing methods do not consider the influences of features in different layers with different anomaly sizes. However, when the defect area is hard to distinguish from the background or the defect area is small, the distance between normal and abnormal features will be too close to detect anomaly areas. Most of the existing methods have achieved considerable anomaly detection performance based on the distance between normal features and abnormal features. Anomaly detection and localization without prior knowledge is a challenging problem in industrial manufacturing due to the complexity and variety of anomaly types. ![]()
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