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Three types of surface detector are available, each based on a different kind of neural network.
Type 1 – Fast:
Detects a wide range of defects, however can suffer from a higher amount of false positives (good parts evaluated as bad by the software).
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Training time is reduced, with bigger & less options of view-finder
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sizes.
This type does not require large datasets, however, performance on highly variable patterns may be suitable for the Precise option.
Type 2 – Experimental:
This type
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may work well for
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smaller defects
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,
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however, it usually requires
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a lot more of training data
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and it is our Experimental type, meaning that may not peform well in some applications.
Training time is usually longer.
Smaller & more precise view-finder sizes
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.
Type 3 – Precise: Very precise heatmaps, does not generate many false positives, it is ideal when searching for smaller defects. Requires more training images & longer training time. (No view-finder size
This type works well with smaller defects such as scratches and others, however, it usually requires more training data to perform well.
Training time is usually longer.
View-finder size not available & ignore function not included
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.
View-finder
Size of the view-finder determines how much the inspection will be focused. The size is selected depending on how detailed inspection model is desired. If you choose a size which is too small, you lose the knowledge of the surroundings and therefore can miss some defects; on the other hand, if chosen size is too large, details can be overlooked.
Just as when a human eye focuses on detecting errors. Some errors are seen from a larger distance, and others can only be seen through a magnifying glass.
Along with the size, the recognition speed also varies. There is no general rule on how to set the right size, you need to try out a number of sizes and learn how to estimate the best size at the first try. The size of the defects you are searching for might be of help.
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