Image Anomaly Detection Using Normal Data Only by Latent Space Resampling

Abstract

Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard to determine the proper dimensionality of the latent space, and it often leads to unwanted reconstructions of the anomalous parts. To solve this problem, we propose a novel method based on the autoencoder. In this method, the latent space of the autoencoder is estimated using a discrete probability model. With the estimated probability model, the anomalous components in the latent space can be well excluded and undesirable reconstruction of the anomalous parts can be avoided. Specifically, we first adopt VQ-VAE as the reconstruction model to get a discrete latent space of normal samples. Then, PixelSail, a deep autoregressive model, is used to estimate the probability model of the discrete latent space. In the detection stage, the autoregressive model will determine the parts that deviate from the normal distribution in the input latent space. Then, the deviation code will be resampled from the normal distribution and decoded to yield a restored image, which is closest to the anomaly input. The anomaly is then detected by comparing the difference between the restored image and the anomaly image. Our proposed method is evaluated on the high-resolution industrial inspection image datasets MVTec AD which consist of 15 categories. The results show that the AUROC of the model improves by 15% over autoencoder and also yields competitive performance compared with state-of-the-art methods.

Image Defect Recognition Method Based on Deep Learning Network

“…Abd El-aziz et al [12] proposed an improved Ostu algorithm weighted object variance (WOV), which ensures that the threshold value is always the value located at the valley of two peaks or at the lower left edge of a single peak histogram, solving the problem of threshold selection in histograms in the case of single and double peaks, and the results show that the WOV algorithm outperforms Ostu, maximum entropy, valley-emphasis, and other algorithms. Wang et al [13] used global thresholding and local thresholding segmentation methods on the preprocessed image to extract the weld region and the defect region, respectively, which can quickly and effectively segment the defects present in the weld image, but the effectiveness of the method is not obvious when applied to the segmentation of weld defects of complex shapes. Cheng and Yu [14] proposed a defect segmentation algorithm based on the removal of the background, which has good results in the segmentation detection of defect areas that cope with defect sizes greater than six pixels without considering the type of defect.…”

Improved Anomaly Detection Based on Image Reconstruction and Global Template Features for Industrial Products

“…By contrast, unsupervised anomaly detection based on image reconstruction or image in-painting needs no the collection of any anomalous samples. AE [1][2][3][4][5][6] (Auto-Encoder), VAE [7,8] (Variational Auto-Encoder), f-AnoGAN [9] and their variants attempt to model the distribution of normality with abnormal samples, by reducing reconstruction error between origin image and reconstructed image. The reconstruction errors of abnormal regions are often supposed to be significantly larger than that of normal ones.…”

Application of Thermography and Adversarial Reconstruction Anomaly Detection in Power Cast-Resin Transformer

“…This structure can define a subspace where only inlier data lie in while outlier data are out. Wang et al [33] adopt VQ-VAE and PixelSnail, respectively, as the reconstruction model and autoregressive model to get the latent representation of normal samples and estimate the probability model of the latent representation. They detect the abnormality by comparing the L2 distance between the restored image and the defective image.…”