When your bread and butter involves integrating and commissioning machine vision systems, it can be easy to forget how alien the technology remains to many people — even those in manufacturing and logistics, where the technology has offered operational benefits for decades. So, it is refreshing when EZ Automation can take a step back from the finer points of engineering to introduce the technology and its benefits to a broader audience. Such an opportunity arose recently, when EZ Automation’s CEO Ahmed Tawfik was invited to chat with Tony Moore on Timpl’s company podcast. In addition to discussing how machine vision is continuously evolving to reshape dozens of industry sectors, the conversation allowed Ahmed to weave in some basics on what machine vision is (and isn’t), explain what’s driving adoption of the technology, and note how vision has become easier than ever to use even as its sophistication has grown. Tony’s podcast underscored just how much curiosity remains surrounding machine vision past, present, and future. So, we elaborate here on some of the basics we discussed with Tony for those of you who found the conversation interesting. If you haven’t listened in yet, there’s a link to the full conversation below.
A Quick Scan of Vision Technology#
At its core, machine vision uses cameras, sensors, computers, and software to automate and enhance execution of tasks such as defect detection, quality inspection, and verifying correct assembly. Vision technology can perform these tasks much more quickly and accurately than humans and without any loss of performance from fatigue. When integrated into a manufacturing or logistics network, vision data can provide process feedback to optimize production. The list of capabilities goes on, but in short, machine vision enables computers to see, analyze, and make decisions with a much higher degree of speed and accuracy than humans. The potential ROI from this has driven adoption of machine vision for decades. Recent advances have further fueled new applications, markets, and users. Among those advances are developments in 3D camera technology and the expanding use of deep learning, a subset of artificial intelligence (AI). Unlike conventional 2D cameras that render image data as a flat plane, 3D cameras capture and render data as detailed 3D point clouds. These point clouds reveal depth, shape, and surface characteristics, significantly extending machine vision’s automated detection of defects to include features that 2D cameras have trouble quantifying. In addition to enhancing almost any task a 2D camera can perform, 3D vision has a wide range of new capabilities and applications. It also enables vision systems to capture more detailed dimensional measurements of packages in a distribution center, guide industrial robots within a 3D model of the real world, or verify no parts are amiss in an assembly. AI has empowered machine vision systems even further. Traditionally, the computers that analyzed captured vision data all relied on rigid rules-based programs that could only perform pass/fail functions. AI has empowered those systems to employ almost human-like analytical abilities. It enables vision to learn and recognize defects or features that the system was not initially programmed to spot — effectively allowing the vision system to improve over time without further human input. AI-powered vision systems are especially valuable in complex and regulated industries, such as medical devices, where early detection of micron-level defects improves yield, reduces costly rework, and supports compliance. When vision engineers first began experimenting with AI, implementing the technology was laborious. It required highly skilled personnel to analyze thousands of images to train the AI to recognize desirable or defective features. The process was time-consuming and largely limited to the library of images the engineer could produce. Paradoxically, as AI has evolved to support more advanced vision systems, it has become easier for nonexperts to use. Pretrained models, simpler training regimens, and streamlined deployment tools have all lowered the technology’s cost and complexity. Importantly, this has made advanced machine vision more available to small- and medium-sized businesses. More manufacturers can now deploy practical, scalable vision solutions without massive upfront investment while keeping systems on premise to protect data and IP. Rather than replacing workers, machine vision takes over the most repetitive inspection tasks and allows teams to focus on higher-value activities such as process improvement, exception handling, and system optimization.
What Vision Can Do for You#
We are grateful to Tony Moore for his curiosity and questions about machine vision technologies. It was a good exercise for us to step back and explain what vision is, what it can do, and how it can empower the modern workforce. Though EZ Automation Systems is on the cutting edge when it comes to engineering and implementing AI-powered machine vision systems, our consulting services target new or less experienced end users who are just beginning to explore how and where vision technology can improve the efficiency, throughput, or quality of their operation. If you’re exploring machine vision, 3D inspection, or AI-driven automation, contact us to learn how our automation consulting services can support your goals. Machine vision has always offered a competitive edge. Now, it is more accessible than ever.
Click here to watch the full podcast with Tony Moore.