For presence checks, components can be rated as ‘good’ or ‘bad’ or divided into 200 classes – for example, to ensure that the right parts for the respective product are always supplied and processed for product variants. 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.
Solving Difficult Problems
The application possibilities of SensoPart’s new AI vision sensor are just as diverse as its built-in classification competence. In automobile production, it can differentiate between component variants and determine whether the appropriate variant is available for a specific vehicle equipment. When flexible, shape-changing objects such as spiral springs or plastic bags are fed in, it detects wrong parts or incorrect positions. Compared to classic detectors, the AI vision sensor can solve such tasks with significantly reduced setup effort and increased process stability. The user saves time because he does not have to create a logical link between several detectors.
The Future of Machine Vision has Begun
The VISOR Object AI makes machine vision easier than ever. With artificial intelligence, the new vision sensor can be set up in just a few steps – without any expert knowledge.
For more information: www.sensopart.com