Covision Quality – User Interface
BRESSANONE, Italy, July 25, 2022 (GLOBE NEWSWIRE) — Covision Quality, a leading provider of visual inspection software powered by unsupervised machine learning technology, today announced it has joined NVIDIA Metropolis – a partner program, application framework and a suite of developer tools bringing to market a new generation of vision AI applications that make the world’s most important spaces and operations safer and more efficient.
The interface of Covision Quality from the perspective of the person ultimately responsible for quality control. In this case, the red border on the image of the manufactured part indicates that the part is “out of order”, so cannot be shipped to the end customer and must be discarded.
Thanks to the unsupervised machine learning technology, the Covision Quality software can be trained in an hour on average and the pseudo scrap rate for its customers can be reduced by up to 90%. The workstations deployed at customers leverage the power of NVIDIA RTX A5000 GPU-accelerated computing, enabling the software to run in real time – processing images, inspecting components and communicating decisions to the PLC. In addition, Covision Quality uses 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 solutions to improve critical operational efficiency and solve security problems. The NVIDIA Metropolis ecosystem includes a large and growing number of members who invest in the most advanced AI techniques and the most efficient deployment platforms, and who take an enterprise-class approach to their solutions. Members have the opportunity to access NVIDIA platform updates early to further enhance and accelerate their AI application development efforts. The program also offers members the opportunity to collaborate with 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 business builder. Covision Quality licenses its visual inspection software product to manufacturing companies in a variety of industries ranging from metal fabrication to packaging. Covision Quality’s customers include GKN Sinter Metals, a global market leader for sintered metal components, and Aluflexpack Group, a leading international manufacturer of flexible packaging.
Franz Tschimben, CEO of Covision Quality, sees significant added value in participating in the NVIDIA Metropolis program: “Participating in NVIDIA Metropolis marks another milestone in our company’s fledgling history and in our relationship with NVIDIA, which began with the our company’s entry into the NVIDIA Inception program last year. It’s a testament to the great work the team is doing in delivering a scalable visual inspection software product to our customers, dramatically reducing ‘time to implementation’ of visual inspection systems and ‘pseudo scrap rates’. We expect NVIDIA Metropolis, which is at the heart of many developments in the industry today, to boost our go-to-market efforts and support us in connecting with customers and system integrators.”
About Covision Quality
Covision Quality licenses its visual inspection software product to manufacturing companies in a variety of industries ranging from metal fabrication to packaging. Thanks to the unsupervised machine learning technology, the Covision Quality software can be trained in an hour on average and the pseudo scrap rate for its customers can be reduced 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 business builder.
For more information, visit www.covisionquality.com
39042 Bressanone, Italy
+39 333 4421494
A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/19998b6c-83b8-41df-8e60-c5d558e3e408