The VISOR Object AI independently learns distinguishing characteristic features based on a few images of the object to be detected. Even strong process and product variations such as fluctuations between batches, contamination, reflections, changing shapes or varying 3D orientation can be taught with just a few mouse clicks. It then is able to reliably recognize the objects appearing in front of the lens and assign them to different classes.
Once a classification has been taught, it works reliably and robustly, without the user having to worry about suitable detection rules and parameters, as is the case with classic, rule-based image processing (e.g., using pattern comparison, contour or contrast recognition). Because the VISOR Object AI is capable of learning, it typically only needs around five sample images per object class to sufficiently achieve a stable detection process. The AI algorithm is implemented in the sensor itself and therefore does not require any network or cloud connections.