Unsupervised fur anomaly detection with B-spline noise-guided Multi-directional Feature Aggregation

Designing an efficient and accurate anomaly detection method is crucial for quality control in medical products, particularly for identifying tiny and complex anomalies such as fur anomalies in medical syringes. In recent years, unsupervised anomaly detection methods based on reverse knowledge distillation have shown superior results. However, these methods suffer from the inability to prevent anomalous information from flowing through the student decoder during inference, leading to incorrect segmentation of abnormal areas. To address this issue, we propose a Multi-directional Feature Aggregation for unsupervised fur Anomaly Detection (MFAAD) method. Firstly, we design a Multi-directional Feature Aggregation (MFA) module, which consists of iterative feature shifting and aggregation operations. Each pixel in the feature map after being processed by the MFA module can acquire global pixel information …