Machine vision is one of those words that can conjure up images of one-dimensional robots making glassware, sword makers, racetracks and even surgical robots.
In fact, the term machine vision is much more than just the use of a camera to capture a vision scene. It is the foundation of a new generation of computer technologies that have led to new iterations of robotics, machine vision software and artificial intelligence. And, as we have seen lately, machine vision is already being applied in business today, and not just in the tech sector.
Machine vision is defined as a computer vision technology that understands three-dimensional objects and can analyze and respond to their shape, motion and movement, as well as to texture, texture and emotional expression.
Machine vision technologies are based on a computer’s ability to perform image classification and identification, such as facial recognition, localization and object classification. They are used to perform image recognition, object recognition, and/or image metadata.
They can analyze images generated from cameras, cell phones, camera sensors and other devices, which provide many benefits in everything from healthcare to manufacturing. Though machine vision technology is known as a complete picture, it also has applications in other fields, such as analytics, robotics, augmented reality, security, logistics, retail, and control. In short, there are applications for machine vision in every field of business.
In particular, business technology leaders are paying close attention to this emerging technology, and are considering integrating it into their strategies to:
Lead by following as it applies to machine vision
Make better business decisions, enabling them to reach growth and profitability
Increase company productivity and efficiency, deliver on customer expectations, and increase profits
Streamline operations to cut costs, and increase earnings
These goals are attractive in many cases, especially among companies looking to get to the right result faster. For example, the National Transportation Safety Board (NTSB) employs machine vision technologies to track and report its progress on fatal truck crashes.
Machine vision can also empower teams to work in agile ways, using a sophisticated, open-source system to understand developments on the fly and implement rapid fixes and modifications on the fly. As I mentioned above, automating repetitive actions can simplify the work of sales teams, supply chain and other employees.
Make observations from a wide range of data
Machine vision technology has multiple uses in the business world. Its profound impact cannot be overemphasized, as we mentioned, because there are so many ways that it can help companies improve their competitiveness and grow their profits. Such benefits include:
Enhanced business analytics. With machine vision analytics in place, businesses can benchmark their progress on a consistent basis and understand how their operations and employees can better meet customers’ needs. Similarly, they can plan logistics improvements by observing problems on the fly, eliminating errors in an instant.
Improved sales and marketing campaigns. Product manufacturers and other technology companies that rely on machine vision sensors can study device shipments and other key sales data to discern discrepancies and improve their customer engagement and success rates. Customers who buy new product can also view specific information regarding the products they bought, as well as related products of similar brands.
Improve process efficiency and reduce costs. Businesses can use machine vision to analyze loads, costs and shipping costs to make sense of their financial performance. In the transportation and logistics industry, this process can improve inventory and asset management while also increasing revenue potential.
Increase workplace safety and productivity. Thanks to machine vision technology, human performance in the workplace can improve while we continue to make gains in safety and productivity. For example, things like ensuring workers use correct maneuvers in the company yard to minimize injuries are all automated by employing machine vision.
Reduce errors and improve speed of decision-making
With machine vision technology in place, businesses can effectively automate tasks, enable more effective collaboration, reduce errors and improve speed of decision-making.
Whichever way it’s implemented, businesses with machines in the workplace (as well as in the data collection realm) stand to benefit greatly.