Manufacturing technology What is computer vision?
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image. Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyse thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.
Source: IBM
increasing digitalisation, with Bharath arguing that machine learning and AI are just two of the technologies that could transform QC over the coming years. From a theoretical perspective, this isn’t hard to appreciate. If, after all, traditional machine vision platforms are programmed to flag specific and measurable errors in any given device – a needle is too long or a pipe too short – machine learning has the ability to make the whole system far more dynamic. Trained across thousands of individual samples, such machines can gradually learn what their human masters are looking for, eventually noticing problems of which they wouldn’t even have thought. This fusion of traditional vision systems with AI is called ‘computer vision’, and there’s plenty of evidence that tech companies are rushing ahead to develop it. At IBM, for example, experts have recently unveiled their Maximo Visual Inspection setup. Among other things, it allows users to constantly improve the power of their vision systems, even training them to understand exactly why something goes wrong.
“I look for a unique optical or visual feature, allowing you to make a robust decision for what it is that you’re trying to measure in the first place.”
Ken McClannon
Yet, if machine learning could one day prod vision systems to even greater heights – with the AI-based medical device ecosystem expected to enjoy CAGR of 25.7% through 2027 – McClannon warns that technology isn’t necessarily a panacea. “I look for a unique optical or visual feature, allowing you to make a robust decision for what it is that you’re trying to measure in the first place,” he says, adding that he prefers a “deterministic” approach to QC. Fair enough: with the human cost of faulty
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equipment potentially fatal, it makes sense that companies might want their QC to deal in absolutes than risking the robots go rogue and claim a fault where none exists. Bharath, for his part, makes a similar point, noting that training machine learning platforms can be cumbersome, requiring manufacturers to supply machines with problematic equipment to ensure they know what they’re looking for.
People power
If insiders are ambiguous about the long-term value of computer vision systems, they’re similarly conscious that humans can never be removed from the equation altogether. To be sure, emphasises Bharath, automation is obviously going to be faster and cheaper than “human observation” in the first instance. But in cases where manufacturers are making small numbers of bespoke devices, potentially tailor-made to individual patients, he suggests it might make more sense to simply check them by hand.
Of course, more mass-produced products can gain much from AI checks, something clear enough if you look at the current buzz of industry activity. Last year, for example, Siemens unveiled SynthAI, a new tool that elegantly dovetails machine vision manufacturing and AI. Among other things, Siemens engineers are experimenting with so-called ‘synthetic data’ – whereby SynthAI is trained on thousands of randomised and computer-generated images. And if that neatly solves issues around how devices are trained, Siemens is far from the only company moving in a similar direction. Philips, for instance, has developed a platform which allows users to send new parameters to a machine vision system even as the process itself is happening. In the case of Philips, that makes it easy for manufacturers to ensure droplet dispensers are as accurate as possible – but it’s obvious that the practical applications of such technologies transcend specific production lines. All the same, McClannon argues that companies
can’t simply rush towards a machine vision future without first considering use cases. While he concedes that AI is “creeping in” across the industry – especially now that the technology increasingly comes with “standard suites” of functions accessible to all – he stresses that device manufacturers are still most concerned with finding “the right tool for each application.” To put it differently, while machine learning can doubtless bolster QC in many cases, deterministic vision systems are unlikely to disappear.
Given how important device quality is to a manufacturer’s reputation – and to patient wellbeing – that’s surely just as well. ●
Medical Device Developments /
www.nsmedicaldevices.com
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