Anomaly Detection of Industrial Products Considering Both Texture and Shape Information

Anomaly detection of industrial products is an important issue of the modern industrial production in the case of shortage of abnormal samples. In this work we design a novel framework for unsupervised anomaly detection and localization. Our method aims to learn global and compact distribution from image-level and feature-level processing of normal images. For image-level information, we present a self-supervised shape-biased module (SBM) aimed at fine-tuning the pre-trained model to recognize object shape information. As for feature-level information, our research proposes a pretrained feature attentive module (PFAM) to extract multi-level information from features. Moreover, given the limited and relatively small amount of texture-based class feature information in existing datasets, we prepare a multi-textured leather anomaly Detection (MTL AD) dataset with both the texture and shape information to shed a new light in this research field. Finally, by integrating our method with multiple state-of-the-art neural models for anomaly detection, we are able to achieve significant improvements in both the MVTec AD dataset and the MTL AD dataset. Our code and dataset are publicly available at https://github.com/DiagoAlaraviz/JigsawBlock .