Innovazione
Deep Learning and Multishot technologies
The analysis of images through Deep Learning opens the way to the possibility of automating an increasing number of industrial processes in a wide range of sectors. From the inspection of surface defects to the sorting of variable parts, to the control of final assemblies, to the evaluation of product quality or to the reading of complex texts.
By applying Deep Learning, we have a system that is able to autonomously classify data and structure them hierarchically, finding the most relevant and useful ones for solving problems and improving its performance with continuous learning – just like the human mind does. Artificial neural networks apply a “deep” learning system, that is, based on a greater number of levels (layers) hidden in the neural network. The “traditional” ones contain 2-3 layers, while the deep neural networks can contain over 150.

The scalability of Deep Learning, thanks to the increase in available data and algorithms, is what differentiates it from Machine Learning. Deep Learning systems, in fact, improve their performance as data increases while Machine Learning applications (or rather, the so-called superficial learning systems), once a certain level of performance has been reached, are no longer scalable even by adding examples and training data to the neural network.
In the world of automatic inspection, it is essential to obtain high quality images. To do this, it is not so much the type of camera, the quantity of lenses or their quality that matters, much less the maximum aperture of the diaphragm. A good image relies on light.
Light management is not always taken for granted and often represents a stumbling block in identifying defects. The main feature of light is to delineate shapes and volumes of objects: depending on how we illuminate it, the physiognomy of the object changes completely, and pieces that were previously compliant turn out to be different.
The Multishot tool allows each camera of the viewing station to obtain multiple images of the same piece in a split of second, each with a different lighting condition.
The advantages? No compromise lighting, guaranteeing extreme precision in identifying defects.
Three images of the same pieces in differt light conditions



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