Prior Normality Prompt Transformer for Multiclass Industrial Image Anomaly Detection

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multiclass anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multiclass scenarios encounters challenges, such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the prior normality prompt transformer (PNPT) method for multiclass image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the 'identical mapping' problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: 1) class-specific normality prompting pool, 2) hierarchical patch embedding, 3) semantic alignment coupling encoding, and 4) contextual semantic conditional decoding. Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multiclass industrial anomaly detection.