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The SEA Vision Group system (under development by a joint team from SEA Vision Group and ARGO Vision) uses the semantic segmentation of the areas of the lipstick (e.g. body, tip, neck, mechanism, etc.) to identify every possible flaw pixel by pixel. This is achieved by classifying areas by categories, each of which is assigned a name or label. Each part or area of the image is classified by categories and identified by a color on the screen to provide the operator with immediate information about the areas being inspected.
The system self-learns how to discern an ever-increasing variety of more and more complex defects, item-by-item. Self-learning takes place both on the basis of proprietary datasets - a mix of real and synthetic images generated with the most advanced data augmentation and neural generation techniques - and by combining the different models and parameters learned over time.