TRANSFORMATIVE TECH: ARTIFICIAL INTELLIGENCE


Harnessing machine learning’s potential


Covision Lab’s grand ambition is to be the leading industrial computer vision machine learning hub. Greg Blackman speaks to its CEO, Franz Tschimben


G


etting the best out of advances in computer vision and machine


learning is a daunting task, considering how much investment is being made in the area, but Covision Lab is bringing together academic research in computer vision with an industrial base to develop applied machine learning platforms. Te goal of Covision Lab, its


CEO, Franz Tschimben, told Imaging and Machine Vision Europe, is to become the leading industrial computer vision machine learning hub in Europe. With headquarters in South


Tyrol, Italy, Covision Lab is a consortium formed in 2019 from seven companies. Tese founding firms cover various sectors, but there is a strong manufacturing presence, which is why one of Covision Lab’s subsidiaries, Covision Quality, is focusing on manufacturing surface inspection. Covision Quality won


the start-up award at A3’s 2022 Automate trade show in Detroit. Covision Quality was also among the leading start-ups as part of the San Francisco Alchemist Accelerator programme, which helped raise its profile in the US. Te research division of


Covision Lab is headed by Professor Oswald Lanz at the Free University of Bolzano Bozen, where partner companies – Nvidia is one – can sponsor PhDs. Tschimben noted that the research branch of the company firstly pushes


boundaries by publishing code for developers, and also, over the long-term, will help Covision Lab attract some of the best computer vision talent. Covision Quality’s technology


is based on unsupervised machine learning, in that the software learns what a good part looks like and deduces the defects from anomalies in the image data. It is not trained with defects, just images of good parts, although often the manufacturer’s CAD model is also used as part of the software pipeline. Covision Lab has two


offshoots, both based on the same unsupervised machine learning technology: Covision Media, which uses the algorithms for rendering sportswear as hyper-realistic 3D models for e-commerce – Gore-Tex uses the software, for example; and Covision Quality for manufacturing inspection. Covision Quality came


about through Alupress’s involvement as one of the founding companies in the consortium. Alupress makes die-cast aluminium parts for the automotive industry. Te focus at the moment for Covision is detecting surface defects, mainly on metals, plastics and packaging, although Tschimben said it plans to expand to inspect other surfaces in the future. Covision Quality is targeting


mid-size manufacturing companies – 2,000 to 15,000 employees – that typically don’t have the specialised personnel to deploy traditional machine vision at scale. Tschimben said:


26 IMAGING AND MACHINE VISION EUROPE VISION YEARBOOK 2022/23


Franz Tschimben, CEO of Covision Lab


‘[Mid-sized manufacturing firms] can have 10 per cent of production lines equipped with machine vision, but they might have difficulties to scale to more because of programming legacy – developing non-machine learning-based visual inspection software is a lengthy, difficult task that requires specialised engineering personnel that is often hard to find and hire. Manufacturers therefore often work with third-party consulting companies. Tat’s where we come in.’ Te unsupervised learning


approach means that specialised personnel isn’t needed and the software doesn’t need to be programmed. Tis makes visual inspection much more scalable, according to Tschimben. One of Covision Quality’s


success stories has been with GKN Sinter Metals, one of the leading sinter metal companies in the world, with thousands of employees globally. Covision Quality began by


implementing its software at one GKN plant in Europe, inspecting a set of metal parts in real time at production speeds


‘We have many companies reaching out to us already to start research collaborations’


of hundreds of milliseconds per part. From this assessment, GKN took the decision to deploy Covision software across multiple production lines at sites in Italy, Germany and the USA. One of the reasons was GKN calculated that Covision’s technology would be 20 times faster at deploying new vision systems compared to traditional methods. Tschimben said it takes roughly a couple of hours to have its software automatically program a visual inspection system, which then can be deployed after going through various accuracy and reliability tests with the customer. One pain point for customers


using traditional vision technology, Tschimben noted, is that it’s difficult to program a


@imveurope | www.imveurope.com


Covision LAb


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