Aiming at the problem of low anomaly detection and localization accuracy in current knowledge distillation-based anomaly detection algorithms due to the low difference in abnormal feature representation between teacher and student models， an anomaly detection algorithm based on bidirectional multi-scale knowledge distillation is proposed. An asymmetric teacher-student network structure composed of a teacher model， a student model and a reverse distillation student model is employed to suppress the student’s generalization to abnormal features. A feature fusion residual module is introduced between the bidirectional distillation student models to integrate multi-scale features and reduce abnormal disturbances. An attention module is introduced within the forward distillation student model to enhance the learning ability of important features. During the testing phase， anomaly assessment is performed through multi-scale anomaly map fusion. Experimental results on the public dataset MVTec AD show that the proposed algorithm， using ResNet18 as the backbone， achieves high scores of 97.7% at the pixel level and 98.8% at the image level on the area under the receiver operating characteristic curve evaluation metric， effectively improving the current knowledge distillation algorithms.
