A Two-stage Industrial Anomaly Detection Method Based on Diffusion Generative Model

Most existing industrial anomaly detection methods adopt the idea of image restoration, which utilizes artificial synthesis of pseudo anomaly samples in the training stage to discriminate the artificial defect image from the reconstructed images after model restoration, and calculates the deviation of the discrimination to obtain the anomalous region. However, the artificial defect images do not correlate well with the actual defect semantics and cannot accurately cover the actual defect types, resulting in less robustness of the model. In order to solve this problem, this paper proposes a two-stage industrial Diffusion Anomaly Detection network (DADNet). Firstly, a semantically-guided anomaly generation module is utilized to synthesize the known defects, and this is used as the a priori information for anomaly detection. In the first stage, an image reconstruction model is used to repair the abnormal region of the workpiece. In the second stage, a discriminative model based on the repaired image and the initial image is trained and used to detect the anomalous regions. In addition, this paper further enhances the performance of the reconstruction model by aggregating features through the joint attention mechanism. DADNet outperforms the existing models on the publicly available datasets of workpieces with different materials, promising prospect for industrial anomaly defect detection.