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There is no need to learn e.g. TensorFlow to successfully apply deep learning. But how complex are actual deep learning solutions for machine vision and which applications are they good for?

Bild: Adaptive Vision - Future Processing Sp. z o.o.Bild: Adaptive Vision - Future Processing Sp. z o.o.
Images 1 + 2 | Modern deep learning networks can be trained with typically 20 to 50 training images within five to ten minutes on a modern GPU platform, e.g. object classification of sushi meals.

From the point of view of a regular user of machine vision systems the topic of deep learning might be confusing. This highly disruptive technology is relatively young and different experts of the field provide contradictory explanations of the subject. Some say that deep learning can behave like a human being, some say it is just sophisticated classification. There is also a question of the skills required for the user to effectively take advantage of it. Does it require a PhD diploma in machine learning or is it suitable just for every regular production worker? In short, each of the answers may be correct depending on the context the speaker has in mind. In this article I will explain it from the point of view of vision-based inspection systems.

The first time I met deep learning in reality was when a colleague of mine showed me how well his brand new phone could translate his voice into written notes. It was a time when I had only seen some early voice recognition systems for cars which had real trouble understanding a simple command such as ´home´. Then the progress was fast and remarkable. Soon after we heard about deep learning being successfully used in recognition of hand-written letters, in classification of objects on images or in programs that played chess better than any program had played before. Today the most interesting commercial topics became medical diagnosis, driving autonomous vehicles, automated harvesting and industrial quality inspection.

Adaptive Vision - Future Processing Sp. z o.o.

Dieser Artikel erschien in inVISION 5 2018 - 30.10.18.
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