Self-supervised Visual Anomaly Detection with Image Patch Generation and Comparison Networks

Automatic industrial anomaly detection, especially visual anomaly detection, is still a challenging task. Taking the product of cloth for example, there are often various intrinsic textures or color patterns on cloth images which makes it difficult to distinguish anomaly and normality. To tackle this issue, we propose a novel self-supervised anomaly detection method consisting of three steps. Firstly, the Vision Transformer-based generation network is trained to learn the product image's texture and color patterns and generate an image patch from the other two adjacent patches. Then, the Siamese-based comparison network is designed to compare the generated patch with the original one to identify and localize the anomaly. Finally, the location of anomaly is refined by a bi-directional inference strategy. Experimental results on both the public dataset MVTec AD and our practical dataset demonstrate the superiority of our method over other state-of-the-art approaches.