A generalized defect-data-free defect inspection method based on image reconstruction and anomaly detection

This paper presents a novel framework based on hierarchical image reconstruction, employing image reconstruction and anomaly detection techniques. Unlike traditional supervised methods, our approach operates without the need for defect-specific training data, enabling generalization across diverse product types. Using hierarchical reconstruction modules and a self-attention mechanism, our method achieves an average precision of 97.83% on the MVTec AD 2D dataset, surpassing the U-Net model by 11.1% and the U-Transformer by 12.9%. Furthermore, the model inference speed reaches 24.1 FPS, representing a 48.1% increase over U-Transformer models. These results demonstrate the framework’s effectiveness in enhancing both detection accuracy and speed, providing a robust solution for real-time industrial defect inspection.