Abstract
In the production of flexible packaging, 200 samples need to be detected per minute, so the speed of detection is very demanding. In this paper, we propose an approach to detect and localize flexible packaging defects with only a small number of defect-free samples for model training. We achieve fast data augmentation by using the spatial transformation network to form an end-to-end detection model. We calculate defect scores by estimating the feature maps density based on the normalizing flow to detect and locate defects. Compared with DifferNet, our approach achieves detection 10 times faster, but AUROC remains almost the same. Thus, our method is more compatible with the requirements of industrial production.
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