Machine Learning, Machine Vision and the Edge

The combination of smart technologies is pushing digital transformation

(Image Source: Siemens)

(Image Source: Siemens)

Machine Vision is fast becoming one of the most central technologies in automation and according to New York-based technology research firm ABI Research shipments of machine vision camera systems will reach 197 million by 2027, with revenue of US$35 billion.  

The report says this surge is driven by machine vision technology merging with machine learning to lead the transition to Industry 4.0. The possibilities are enormous, especially at the edge.   

“The shift from machines that can automate simple tasks to autonomous machines that can ‘see’ to optimize elements for extended periods will drive new levels of industrial innovation,” says David Lobina, Artificial Intelligence and Machine Learning Analyst at ABI Research. 

“This is the innovation can augment classic machine vision algorithms by employing the range and reach of neural network models, thus expanding machine vision far beyond visual inspection and quality control,” he says.   

The report says the edge of computing has the most exciting applications and benefits for the combination of machine learning and machine vision—namely, in those devices that are part of embedded systems and the Internet of Things. Smart manufacturing is perhaps the most straightforward case, where smart cameras, embedded sensors, and powerful computers analyze every process step.  

Smart machine vision is already on the job in factories, warehouses, and shipping centers, aiding and assisting human workers by handling the more mundane tasks and freeing up workers to use their expertise to focus on the essential tasks. The market is also ripe for development in smart cities, smart healthcare, and smart transportation, with ATOS (in cities), Arcturus (in healthcare), and Netradyne (in transportation) as some of the key vendors in these sectors. 

As in other cases of edge machine learning applications, the best way for the technology to advance is through a combination of hardware and software solutions and employing information-rich data. The key will be how to merge all of these elements into a package that provides hardware (cameras, chips, etc.), software, and a way to analyze the data. The “whole package” approach is perhaps not the most common example in the market, but vendors must be increasingly aware of how their offerings can mesh with other solutions, which will often require providing hardware-agnostic software as well as software-agnostic data analysis.