BRESSANONE, Italy, July 25, 2022 (GLOBE NEWSWIRE) — Covision Quality, a leading provider of visual inspection software based on unsupervised machine learning technology, today announced it has joined NVIDIA Metropolis — a partner program, application framework, and set of developer tools that bring to market a new generation of vision AI applications that make the world’s most important spaces and operations safer and more efficient.
Covision Quality’s interface from the perspective of the end-of-line quality control operator. In this case, the red border on the image of the manufactured part indicates that the part is “not OK”, thus can not be sent to the end customer and needs to be discarded.
Thanks to its unsupervised machine learning technology, the Covision Quality software can be trained in an hour on average and generates reduction of pseudo-scrap rates by up to 90% for its customers. Its workstations that are deployed at customer sites harness the power of NVIDIA RTX A5000 GPU-accelerated computing, which allows the software to run in real time — processing images, inspecting components, and communicating decisions to the PLC. In addition, Covision Quality leverages NVIDIA Metropolis, the TensorRT SDK, and CUDA software.
NVIDIA Metropolis makes it easier and more cost effective for enterprises, governments, and integration partners to use world-class AI-enabled solutions to improve critical operational efficiency and solve safety problems. The NVIDIA Metropolis ecosystem contains a large and growing breadth of members who are investing in the most advanced AI techniques and most efficient deployment platforms, and using an enterprise-class approach to their solutions. Members have the opportunity to gain early access to NVIDIA platform updates to further enhance and accelerate their AI application development efforts. The program also offers the opportunity for members to collaborate with industry-leading experts and other AI-driven organizations.
Covision Quality is a spin-off of Covision Lab, a leading European computer vision and machine learning application center and company builder. Covision Quality licenses its visual inspection software product to manufacturing companies in several industries, ranging from metal manufacturing to packaging. Customers of Covision Quality include GKN Sinter Metals, a global market leader for sinter metal components, and Aluflexpack Group, a leading international manufacturer of flexible packaging.
Franz Tschimben, CEO of Covision Quality, sees an important value-add in joining the NVIDIA Metropolis program: “Joining NVIDIA Metropolis marks yet another milestone in our company’s young history and in our relationship with NVIDIA, which started with our company joining the NVIDIA Inception program last year. It is a testament to the great work the team is doing in providing a scalable visual inspection software product to our customers, drastically reducing ‘time to deployment’ of visual inspection systems and ‘pseudo scrap rates.’ We expect that NVIDIA Metropolis, which sits at the heart of many developments that are happening in the industry today, will give us a boost in our go-to-market efforts and support us in connecting to customers and system integrators.”
About Covision Quality
Covision Quality licenses its visual inspection software product to manufacturing companies in several industries, ranging from metal manufacturing to packaging. Thanks to its unsupervised machine learning technology, the Covision Quality software can be trained in an hour on average and generates reduction of pseudo-scrap rates for its customers by up to 90%. Covision Quality is the recipient of the Cowen Startup award at Automate Show 2022 in Detroit, United States.
Covision Quality is a spin-off of Covision Lab, a leading European computer vision and machine learning application center and company builder.
For more information, visit www.covisionquality.com
A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/19998b6c-83b8-41df-8e60-c5d558e3e408
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