Cross-Domain Transfer and Few-Shot Recognition of Defect Images in Complex Industrial Settings

When using the few-shot recognition model, due to the influence of materials, light directions and background textures of the industrial inspection images, there will be domain drift, and the recognition accuracy of the model will be unstable when used in different equipment. We built a new dataset with 54, 000 images of 5 types of equipment and 5 types of defects to improve the generalization ability of defect recognition in new equipment scenarios. We present a cross domain few shot recognition model that combines ConvNeXt texture features extraction, domain adversarial feature alignment and prototype network discrimination. The experimental results demonstrate that the model gets the average accuracy of 88.3% and F1 score of 84.7% in defect classification with 50 labeled target samples per class. For the unseen equipment, the recall rates for superficial cracks and pitting corrosion were 81.6% and 85.2%, respectively. The results show that the method can further improve the cross-domain aggregation function for similar defects and improve the stability of the model transfer and deployment in complex industrial scenes.