Smart cameras, embedded sensors and powerful computers can bring machine learning analyses to every process step. Smart machine vision is already on the job in factories, warehouses and shipping centers, aiding and assisting human workers by handling mundane tasks, freeing workers to use their expertise to focus on essential processes. The market is also ripe for development in smart cities, smart healthcare, and smart transportation.

As in other cases of edge-based machine learning applications, the best way for the technology to advance is through a combination of hardware and software technology and employing information-rich data. In cases involving sensitive or private data, such as healthcare, a whole package should provide 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. Still, vendors must be increasingly aware of how their offerings can mesh with other technology, often requiring hardware-agnostic software and software-agnostic data analysis. 

“This is a crucial point in the case of smart cities, healthcare, and transportation, especially regarding what machine vision can achieve in all these settings. For edge vision, software and hardware vendors, as well as service providers, will start taking an expansive view of the sector,” Lobina says.