All good questions - so the severity of surfaced defects are basically all just differences in the pixel color - the resulting "how much they matter" is basically dependent on training data at the beginning and as the system is trained in an ongoing basis. Basically if there is knowlegable training data going in - and then synthetic training data created from there - then all these things get distinguished . Glossy surfaces are definitely the most nuanced but in short - if a defect or other factor changes the pixel colors enough in a card to impact a grade then the logic of how those pixels got disrupted gets built in.
So if the the disruption is meant to be there then the training data should show it.
- if the training data is good. My guess is also that any surface defect or difference would be visible straight ahead in a high res photo - even if it's not visible to the naked eye. Even seemingly straight indents or stamps are never actually 100% straight. In general surface and particularly glossy surfaces will be a bit more nuanced - but in machine vision the accuracy is really dependent on your training data and how you amplify it.
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