Knowledge distillation with attention mechanism for anomaly detection

In the industrial domain, accurate detection and localization of abnormal images are crucial factors for ensuring production efficiency and quality. In recent years, methods in the field of unsupervised anomaly detection have predominantly centered around knowledge distillation models with a teacher-student structure. Although the application of knowledge distillation has significantly improved the precise identification of abnormal regions compared to traditional methods, the currently best-performing knowledge distillation models exhibit identical architectures for both the teacher and student networks. This limitation hampers the effective lightweighting of the student network and poses challenges in distinguishing between background and target objects in certain data with backgrounds, thereby affecting accuracy and wasting computational resources. To address this issue, we propose an innovative approach that integrates knowledge distillation with attention mechanisms for identifying abnormal regions in industrial anomaly images. Our method comprises two key features: firstly, transferring knowledge from a pretrained teacher network to a student network to enhance performance; secondly, incorporating attention mechanisms to direct the model's focus towards potential abnormal regions, thereby enhancing detection accuracy. Our approach innovatively combines knowledge distillation with attention mechanisms, offering a novel solution for industrial anomaly image recognition. Through experimental validation, our method demonstrates superior performance compared to the original models, providing fresh insights for the field of industrial anomaly image analysis.