Technologies advancing machine vision identification

A number of technologies are pushing the machine vision industry forward in regards to identification capabilities. Identification is a central aspect of what machine vision systems have to offer in nearly any scenario – these technologies expand upon the most basic capabilities of machine vision.

Machine vision has played an important role in many different manufacturing processes through the years. The technological changes currently underway promise to have wide-reaching impacts across dozens of industries.

Understanding machine vision identification

Identification, as mentioned, is a core competency of machine vision. In order to provide any valuable information about a given image or environment, a machine vision system, at the most basic level, must be able to identify pre-programmed characteristics or objects.

This may come in the form of inspection to detect the presence or absence of an item, or whether it has defects from assembly. It may be in the form of measurement of objects for quality assurance. It may be used to locate objects, such as randomly oriented objects for robotic guidance, or it can even involve the differentiating of closely related objects for automated sorting.

Machine vision technologies for better identification

There are several new machine vision technologies allowing for better identification. Chief among them is the emergence of 3-D machine vision systems. In most instances, 3-D vision systems are able to detect objects in far greater detail an 2-D vision systems. Whether it’s for more advanced model matching in inspection applications, or for better object differentiation in metrology applications, 3-D vision systems bring more advanced capabilities to the table.

Hyperspectral imaging and color imaging are also important improvements over traditional monochrome imaging. Hyperspectral imaging allows machine vision to detect features beyond the visible light spectrum for more powerful imaging, while color imaging allows for advanced color analysis in inspection applications.

Further, deep learning is starting to become an important technology in advancing machine vision identification. This is primarily through more sophisticated techniques for object detection and object classification that allow machine vision systems to collect more context from the surrounding environment.

Machine vision identification is a core process in nearly any machine vision application. The new technologies mentioned above are primarily responsible for improving identification capabilities, and are pushing machine vision into a brighter future.

This article originally appeared in Vision Online. AIA is a part of the Association for Advancing Automation (A3), a CFE Media content partner. Edited by Chris Vavra, production editor, CFE Media, cvavra@cfemedia.com.

Original content can be found at www.visiononline.org.

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