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197 million smart machine vision cameras at the edge by 2027: ABI Research

197 million smart machine vision cameras at the edge by 2027: ABI Research

According to ABI Research, a global tech intelligence firm, the deployment of smart machine vision (MV) at the edge is expected to reach 197 million or $35 billion by 2027. The firm says increased adoption of edge ML-based MV technology will bring efficiency to factories, warehouses, shipping centers, healthcare facilities and transportation systems.

Machine vision (MV) technology enables industrial machines to observe and assess tasks with remarkable speed, allowing for swift decisions based on what the system sees. ABI Research notes that machine vision (MV) is rapidly becoming a key technology in automation. As MV and machine learning (ML) unite to initiate the shift to Industry 4.0, the possibilities at the edge are numerous.

Smart manufacturing is one example that can benefit from the implementation of edge ML-based MV. Here, sophisticated cameras, built-in sensors and powerful computers can efficiently bring machine learning analysis to every process step. David Lobina, an AI and machine learning analyst at ABI Research, says that factories can benefit from the innovation these systems provide.

“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. This is the innovation that ML offers to MV (also often known as computer vision). ML can augment classic machine vision algorithms by employing the range and reach of neural network models. In turn, it expands machine vision beyond visual inspection and quality control, the locus classicus of good, old-fashioned computer vision,” stated Lobina.

There is also potential for growth in the smart cities, healthcare and transportation sectors, with Atos (cities), Arcturus (healthcare) and Netradyne (transportation) as some of the top vendors in these fields.

The report also shows that companies must be aware of how their offerings can mesh with other vendor solutions. The “whole package” approach is currently not the most common example in the market. However, a complete package may be a good idea when it comes to safeguarding sensitive and personal information, such as in the healthcare field. That includes hardware (cameras, chips, etc.), software and an effective way to analyze data.

Further, hardware-agnostic and software-agnostic data analysis will become increasingly crucial as edge ML-based MV solutions grow.

“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 MV, software and hardware vendors, as well as service providers, will start taking an expansive view of the sector,” Lobina adds.

ABI Research’s findings are based on its Edge ML-Based Machine Vision Software and Services application analysis report, which delves into the market trends driving the adoption of edge ML-based MV technology. Through primary interviews with vendors and customers of machine vision solutions, the company says its report and associated services provide research, data and ABI insights on today’s transformative technologies.

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