Vision & Imaging Blog
Artificial Intelligence Applications in Industrial Machine Vision
The idea of artificial intelligence (AI) has been around since the 1950s and typically conjures up images of fully conscious robots. Technically, artificial intelligence is really a machine’s ability to replicate human intelligence through machine learning.
In that sense, machine learning and deep learning fall under the AI umbrella – a machine’s ability to learn from experience and act without specific programming. These concepts have major implications for the world of industrial machine vision.
When Will AI Be Used For Machine Vision?
Even conservative estimates from machine vision experts say the full power of AI is expected to penetrate the machine vision industry within 3 to 5 years. Companies such as ViDi Systems already have machine vision solutions (the ViDi Suite) that leverage AI on the market. This is exciting new technology with enormous potential to enhance the capabilities of machine vision systems.
Market Intelligence News & Insights:
How Will Apple’s $500 Billion Investment for US Manufacturing, Education, and AI Play Out?
On February 24th, Apple announced a “commitment… to spend and invest more than $500 billion in the U.S. over the next four years”, with plans for new manufacturing plants, enhanced support for manufacturing partners via their Advanced Manufacturing Fund, and a myriad of other initiatives. As part of this plan, Apple projects 20,000 new hires, with roles focused on R&D, silicon engineering, software development, and AI/ML engineering.
What is Machine Vision AI For? How Does it Work?
Artificial intelligence brings new levels of repeatability to machine vision inspection. Current machine vision systems are heavily dependent upon a rules-based system where variability in the inspected parts is low. The benefits of AI allow for much larger variability in inspected parts with high levels of accuracy.
This is achieved through machine learning and deep learning. If we look at the ViDi Suite, for example, the entire AI machine vision system grows smarter as it executes the various stages of the inspection process. Stage one finds, detects and recognizes a single or multiple features within an image. In the next stage, the machine learns to detect anomalies in this image, and then continues to learn, through the following stage, to separate the images into different classes to identify different objects. In a nutshell, throughout the inspection process, the data collected is stored and analyzed by the machine, effectively making it self-teaching and ‘smarter.’
There are many exciting possibilities for AI in machine vision.
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