Enhancing Industrial Anomaly Detection with RealNet: A Unified AI Framework for Realistic Anomaly Synthesis and Efficient Feature Reconstruction

RealNet is a cutting-edge framework for self-supervised anomaly detection in industrial image analysis. It incorporates Strength-controllable Diffusion Anomaly Synthesis (SDAS) for realistic and diverse anomaly generation, Anomaly-aware Features Selection (AFS) for feature reduction, and Reconstruction Residuals Selection (RRS) for adaptive anomaly identification. RealNet outperforms existing methods and introduces the Synthetic Industrial Anomaly Dataset (SIA) for improved anomaly synthesis.