Machine Vision: Existing Trends and Challenges

Machine Vision: Existing Trends and Challenges

Calem Harris, Director of Controls and Automation Engineering, Staples

Calem Harris, Director of Controls and Automation Engineering, Staples

The understanding and utilization of machine vision has changed drastically as industries have found more use case scenarios to apply the technology and science towards. It is now used in medical, military, supply chain, manufacturing, packaging, transportation, and in commercial industries just to name a few. For the technology to continue to expand and be implemented in a manner that optimizes the use of machine vision the existing trends and challenges must be shared and discussed by technology leaders globally.

Machine vision is the pairing of digital cameras with a computer system that can process the data and turn the data into meaningful action such as face or object recognition. The goal of machine vision is to automate the processing of digital images to eliminate the need for human interaction or involvement within a process. With rapid technological enhancements related to digital cameras and computer processing machine vision has been widely used in more industries to accomplish more complex image processing. When machine vision was first leveraged in facial recognition systems nearly two decades ago physical automation was at that time just a dream. However, technology leaders are now pairing machine vision, machine learning, and physical automation to accomplish even the most complex of tasks.

The integration of robotic control systems and machine vision is exploding. These investments are becoming significantly more lucrative and viable to business leaders as labor markets continue to see a decrease in employees that are willing to perform manual functions, while at the same time the minimum wage continues to increase. Industry giants are therefore turning to machine vision and robotics to fill these labor voids and decrease their operational costs. These applications are most utilized to identify an object, differentiate certain features about that object, and then perform a certain physical function that is normally done by a human. In ecommerce these solutions are paired to pick items to fulfill customer orders; in manufacturing products are being picked and placed into packing then inspected for quality control; in the medical field they are being utilized to pick up instruments on demand and used to help identify potential organ degradation the human eye may not detect; in agriculture the picking, inspection, and packaging of agriculture are all accomplished with machine vision paired with robotics. The applications machine vision can be used with is almost limitless.

"The integration of robotic control systems and machine vision is exploding as labor markets continue to see a decrease in employees that are willing to perform manual functions, while at the same time the minimum wage continues to increase"

The challenge with machine vision being used to accomplish certain physical tasks is the environment. There are significant variations to all physical objects that as humans we have learned to interpret through years of trial and error, but also though the use of additional senses. For example, when we see an object, we know what its texture feels like. We have touched plastic, leather, metal, and paper before. We can identify that it has a smooth or rough surface even through different lighting variations, because our brains intuitively utilize our previous experience with these objects to determine the best method of picking them up. When a machine vision application is determining what an object is, and its orientation, it is looking for distinguishing features that can be associated with edges and contour. It does this by turning on variable lighting or sensors that are paired with the application that gives it the best chances of success for determining these features. However, if the lighting is positioned incorrectly, there is dust or water causing the lighting on the object to be distorted, or the item has a reflective material the machine vision application will have difficulty detecting the objects features correctly.

These challenges are less of an obstacle when the machine vision application operates in the same lighting and environmental conditions, and it is used to identify the same object. Contrary, an application that must identify and handle multiple if not thousands of different items, like in supply chain, the success rate declines drastically. In applications that have consistently changing variability technology leaders must incorporate complex machine learning into their application while spending the appropriate amount of time teaching the application what it is looking at. As future iterations of the application are tested the system can leverage the attributable parameters to make the best determination on how to fulfill the output expected.

Machine vision will remain in high demand to be used in applications that automate a process. To implement the application correctly it takes considerable time, focus, and flexibility to implement a system that can perform the expected function. Technology leaders must be willing to embrace machine vision for all its capabilities and shortfalls as technological enhancements continue to add value to machine vision applications.

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