Artificial intelligence in machine vision applications
Artificial intelligence (AI) technology is beginning to make its way into machine vision applications in a wide range of industries thanks to the rise of the Industrial Internet of Things (IIoT).
Artificial intelligence (AI) technology is beginning to make its way into vision applications in a wide range of industries, expanding on existing capabilities and opening up entirely new possibilities in machine vision.
AI technology primarily comes in the form of machine learning and deep convolutional neural networks to help vision systems learn, distinguish between objects and even recognize objects. AI is helping bring machine vision technology into new territory.
One primary reason for the use of AI in machine vision systems is the rise of the Industrial Internet of Things (IIoT). The IIoT features machine-to-machine communication in a highly automated environment that’s dependent upon machine vision to identify a wide range of objects within the factory and throughout the process of the flow of goods.
Further, a vision system’s ability to actually recognize objects, as well as a variety of defects in an object, can significantly improve the accuracy of a vision system. For inspection applications, for example, this accuracy translates directly into productivity and profitability.
How AI is used in machine vision systems
AI can be used in numerous ways along with vision systems. As mentioned above, inspection applications are some of the first jobs that AI has been profitable in, specifically when leveraging machine learning algorithms for defect detection and classification. The cost of acquiring and labelling large datasets has decreased in the past few years due to advances in IIoT, making machine learning more accessible than ever for inspection applications.
The other way AI is used in vision systems is for continuous improvement in recognition applications. This could be deployed in nearly any scenario in which vision systems are used for object recognition. Typically, incorrect predications can be identified and associated with recorded data, so a vision system can continuously learn and improve itself based on its own mistakes.
Of course, there are many other ways in which AI can be used in tandem with vision systems, but inspection applications and continuous improvement for object recognition tasks are some of the most common and practical uses.
This article originally appeared on the AIA website. The AIA is a part of the Association for Advancing Automation (A3). A3 is a CFE Media content partner. Edited by Chris Vavra, production editor, Control Engineering, email@example.com.