Semi-supervised Knowledge Distillation for Tiny Defect Detection

Image anomaly detection can automatically detect defects using images of products, which is crucial for product quality controls. Because of insufficient abnormal data, unsupervised image anomaly detection based on knowledge distillation has attracted broad attention recently. However, fully unsupervised methods suffer from detecting tiny anomalies that widely exist in industrial products because the features of tiny anomalies and normal features extracted by the teacher network are similar. This paper extends current unsupervised anomaly detection methods into a semi-supervised manner, simultaneously leveraging normal data and a limited amount of abnormal data. An automobile plastic parts dataset is established to prove the effectiveness of the proposed method. Experiments show that the proposed method can accurately detect small anomalies and largely surpass a powerful baseline (6% in AU-ROC, 10% in F1-score, 11% in Accuracy).