Detekcija logičnih anomalij z uporabo velikih jezikovnih modelov

Anomaly detection is essential in industrial inspection and has recently been divided into two tasks: structural and logical anomaly detection. Structural anomaly detection focuses on visible defects such as dents or scratches, while logical anomaly detection identifies inconsistencies such as incorrect object combinations. Unlike structural anomalies, logical anomalies cannot be easily identified from a single image, as they often require an understanding of contextual relationships. We propose a new problem: Zero-shot Logical Anomaly Detection, in which only category-specific logical constraints in text form are provided at training time. The model must then determine whether an image complies with these constraints, without having seen any normal or anomalous samples. To enable this, we extend two existing datasets, MVTec LOCO and CAD-SD, with constraint annotations. We also propose a method based on Large Language Models (LLMs), prompted with chain-of-thought reasoning, to assess compliance with the given constraints. Our approach achieves AUROC scores of 69.8% on MVTec LOCO and 99.4% on CAD-SD, demonstrating the potential of LLMs in anomaly detection without visual training data.