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This type may work well for smaller defects, however, it usually requires a lot more of training data and it is our Experimental type, meaning that may not peform perform well in some applications.
Training time is usually longer.
Smaller & more precise view-finder sizes.
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Surface detector of Type 1 usually works better using bigger size of viewfinder view-finder than what would work with Type 2. The Type 3 surface detector doesn't use this parameter.
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The detection result is a set of heatmaps (one for each class). When validating, heatmaps are plotted in the image for better illustration. In the heatmaps, the searched-for areas surrounded by rectangles are added to the context to ‘detectedRectangles’. Each rectangle has a class assigned, depending on which heatmap it was found in.
Evaluation
We can set the rules for all classes or individually. There are three options for the evaluation rules:
Count - number of defects (rectangles)
Edge length - length of the edge of individual rectangle
Area - sum of pixels (of given class if it’s selected) in the whole heatmap