Unsupervised Anomaly Detection on Custom Silk-Screen Printing Patterns with Generative Models

Anomaly detection in industrial vision systems faces significant challenges due to the domain mismatch with large-scale datasets like ImageNet and the limited availability of labelled anomalies. This paper presents a reconstruction-based approach to detect defects in silk-screen printed glass, a domain characterized by structured patterns and scarce training data. We propose a two-stage approach: first, we use a zero-shot, class-agnostic segmentation model (Segment Anything) to generate candidate regions in the test image, then identify the region of interest by matching its features to those of a template region using feature similarity. Secondly, from the segmented areas, we extract patches to train and evaluate three generative models: a variational autoencoder (VAE), a generative adversarial network (GAN), and a DRAEM-inspired dual-network architecture with synthetically generated noise. We validate our methods on two custom datasets - S10 and M12 - collected from grayscale scans of glass plates. Results show that the GAN model consistently outperforms others, achieving high recall and precision even with limited training data, while DRAEM's reliance on synthetic noise impairs its generalization to real defects. Our study underscores the effectiveness of reconstruction-based methods in specialized industrial scenarios where conventional pre-trained features and synthetic anomalies may fall short.