Few Shot Ultra Fine-Grained Visual Classification for Defect Detection

The challenge in defect detection lies in accurately classifying various defect types, each minute in size, and similar defects. Moreover, as defect images are scarce, defect detection becomes a few-shot sample task falling under fine-grained visual classification (FGVC). However, in practical scenarios, discerning defects for model differentiation is complicated by the need to capture subtle features and nuances among different defect classes. Consequently, effective defect detection demands models with robust feature extraction capabilities for few-shot sample tasks to resolve the challenges of FGVC. To address this, we integrate the Self-supervised Knowledge Distillation (SKD) with the few-shot learning model to enhance the feature extraction capabilities of models in handling few-shot FGVC. According to our experiments, our proposed method significantly enhances average accuracy by 5.5%. Hence, this approach enables the rapid training of highly accurate models using limited samples.