LAUSR

Aiming at the problem that traditional surface detection is easily affected by complex industrial environments and cannot extract effective features, a deep learning-based knowledge distillation anomaly detection model is proposed. Firstly, a pre-trained teacher network was used to transfer knowledge of normal samples to the student network in the training phase. In the testing phase, defect detection was achieved based on the feature differences in the output of the teacher-student network for abnormal samples. Secondly, the attention mechanism module and the feature fusion module were added to the teacher network, which enhanced the detection ability of various defects of different sizes and increased the difference in output features between the teacher network and the student network. Finally, the defect image was located at the pixel level. Compared with the advanced knowledge distillation methods, the experimental results of the proposed model on the detection on MVTecAD and the anomaly localization on MVTecAD showed that the method in this paper improved to 90.3% and 90.1% on AUROC respectively, which verified the effectiveness of the method.