Machine vision technologies based on deep learning and CNNs can be used profitably in many different branches of industry and applications. In the electronics industry, the inspection process can be automated and accelerated. With the help of self-learning methods, therefore, all conceivable product defects can be effectively detected - as described above.
Even the tiniest scratches or cracks in circuit boards, semiconductors, and other components are reliably identified, which allows the removal of corresponding parts to be automated.
The food and beverage industry benefits from deep learning technologies, too. For example, poor-quality fruits and vegetables can be detected more precisely before they are packaged or further processed.
The processes are also used in automotive engineering. This industry, in particular, is characterized by an especially high degree of automation. Here, for example, self-learning algorithms perfectly identify tiny paint defects that are not visible to the naked eye.
Another important area of application is pharmaceuticals. Pills often look very similar on the outside, but contain entirely different active substances. Through deep learning and CNNs, the drugs can be very reliably identified, inspected, and distinguished from each other so they are always placed in the correct blister packs.