Feature: Machine vision


Visual inspection enters a new era, thanks to sophisticated deep-learning systems By Miron Shtiglitz, Director of Product Management, QualiSense


S


imple rule-based algorithms have been used in the visual inspection market for many years, but as their limitations become more obvious, the need for


more sophisticated soſtware grows. In principle there are two eras of


machine learning in visual inspection – but, now, we are entering a new one, a third era, characterised by various levels of semi-supervised deep learning. Many of the most-common solutions available in the visual inspection market


can be characterised as rule based. Tey operate with different algorithms and the parameters are set by an expert. For example, the rule might be to count how many black pixels are in an image and if this is above a particular number flag the image as a defect. Solutions like this have been used for


at least 25 years, and are very effective at simple tasks. However, for more complex problems, such as those routinely encountered in fields like surface visual inspection, rule-based solutions are inadequate.


44 July/August 2023 www.electronicsworld.co.uk Relatively small deviations, like


printer ink fading in the image, lighting conditions changing, or even a simple change in a supplier material would seriously impact the efficacy of rule-based systems. Ten, quality managers must call a service team to continuously update the parameters. Clearly there’s a need for a more


sophisticated yet less rigid technology that will overcome these problems and allow more tolerance in its application. Hence, we’ve now entered the era of


deep learning.


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