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RAMFAE: a novel unsupervised visual anomaly detection method based on autoencoder
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DOI：10.1007/s13042-023-01913-7.png)
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摘要
En 中文
Traditional methods of visual anomaly detection based on reconstruction often use normal data to train autoencoder. Then the metric distance detection method is used to estimate whether the samples of detection belong to the exception class. However, this method has some problems that the autoencoder produces blurry images to cause false detection of normal pixel points. The model may still be able to fully reconstruct the undiscovered defects due to the large capacity of autoencoder, even if it is trained only on normal samples. Then, the metric distance detection method would ignore local key information. To solve this problem, this paper comes up with the random anomaly multi-scale feature focused autoencoder (RAMFAE), an innovative unsupervised visual anomaly detection technique, which incorporates three novel concepts. First, a multi-scale feature focused extraction (MFFE) network structure is designed and added between the encoder and decoder, which effectively solves the problem of reconstructing image blur and effectively improves the sensitivity of the model to normal regions. Second, this article employs Delete Paste, a novel data augmentation strategy for generating two different types of random anomalies, which pastes the cut part into a random location, while the pixels in the original position are filled with 0. In spite of the input anomalous images, the strategy makes the model be able to produce normal images to avoid the phenomenon of anomaly reconstruction, and then enables defect localization based on the error between the measured image and the reconstructed image. Third, the study adopts the image quality assessment with combining gradient magnitude similarity deviation (GMSD) and structural similarity (SSIM) to solve the problem that local key information and texture detail information are not easy to be paid attention to by the model, and alleviate the training pressure caused by Delete Paste enhancement. We perform an extensive evaluation on the challenging MVTec AD data set and compare it with the advanced visual anomaly detection methods in recent years as well. The AUC final result of RAMFAE in this text reaches 94.5, which is 3.6, 2.5 and 0.8 higher than the advanced IGD, FCDD and RIAD detection methods.
Keyword:
Anomaly detection
Data enhancement
Image quality assessment
Unsupervised learning
Multi-scale feature extraction
Attention mechanisms
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2.7
论文数:
3.2K
被引数:
5.6K
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