GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module

TL;DR

A new architecture composed by two blocks of a generative adversarial network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image segmentation, spotting defects.

Abstract

Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspection for surface anomaly detection, which aims to spot regions that deviate from regularity and consequently identify abnormal products. Defect localization is a key task, that usually is achieved using a basic comparison between generated image and the original one, implementing some blob-analysis or image-editing algorithms, in the post-processing step, which is very biased towards the source dataset, and they are unable to generalize. Furthermore, in industrial applications, the totality of the image is not always interesting but could be one or some regions of interest (ROIs), where only in those areas there are relevant anomalies to be spotted. For these reasons, we propose a new architecture composed by two blocks. The first block is a Generative Adversarial Network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image segmentation, spotting defects. This method learns from a dataset composed of good products and generated synthetic defects. The discriminative network is trained using a ROI for each image contained in the training dataset. The network will learn in which area anomalies are relevant. This approach guarantees the reduction of using pre-processing algorithms, formerly developed with blob-analysis and image-editing procedures. To test our model we used challenging MVTec anomaly detection datasets and an industrial large dataset of pharmaceutical BFS strips of vials. This set constitutes a more realistic use case of the aforementioned network.

Figures (12)

• Figure 1: Simulated anomaly generation process. In (a) there is an example of Perlin noise. (b) represent the merging of the anomaly map and a random RGB pixels. (c) represent and example of a image with generated fake anomalies.
• Figure 2: The architecture of DRÆ M GAN. The architecture is quite similar to vanilla DRÆ M, but we can see the implementation of GANomaly instead of the AE which acted as the Reconstructive network.
• Figure 3: Train step flowchart: input image $X$ is transformed in $X_n$, that is the image with the Perlin noise superimposed. $M$ is the mask image of the noise areas.
• Figure 4: Inference step flowchart.
• Figure 5: Two consecutive residual blocks of one stage of the encoder network. The introduction of a residual architecture in the encoder-decoder-encoder GAN DBLP:journals/corr/HeZRS15 revealed to be more stable during training phase, by giving better results with equal epochs.