Utilising the cloud to make better use of deep learning machine vision technologies, overcoming data and factory site silos to enhance the volume and variety of data in high value workflows, such as testing and quality control. Donato Montanari, VP and GM of Zebra Technologies, explains more.
When it comes to developing new and existing factories and procuring solutions, the focus is at site level, with input and sign-off shared at site and corporate level. But there’s always the possibility of different sites using different solutions for similar workflows, and the risk of expertise and data not being shared across sites, including when using newer artificial intelligence (AI)-powered solutions where data quality is essential. This can also be true for visual inspection teams using machine vision systems for quality and compliance.
According to this Zebra report examining AI machine vision in the automotive industry, almost 20% of automotive machine vision leaders in Germany and the UK say their AI machine vision could be working better or doing more.
Machine vision teams across manufacturing industries need new ways to leverage deep learning machine vision, which should include using the cloud. A cloud-based machine vision platform would allow teams to securely upload, label, and annotate data from multiple manufacturing locations across site, country, and region. A larger, more diverse range of pooled data in a cloud-based platform from across sites and environments is better for deep learning training. Such a platform would allow defined users to work together in real time, collaborate on annotation, training and testing projects, and share their expertise.
With a cloud-based platform, users with defined roles, rights and responsibilities could train and test deep learning models in the cloud. Powered by much better training and testing data, they may deliver much higher levels of visual inspection analysis and accuracy beyond conventional, rules-based machine vision for certain use cases. These outcomes are sought by manufacturers in the automotive, electric battery, semiconductor, electronics and packaging industries, to name a few.
A cloud-based solution also delivers scalability and accessibility of computing power. With traditional systems, select employees get powerful GPU cards to perform training. With the cloud, every user can access the same high computing power from their laptops. While cloud solutions cost, through a pay-as-you-go subscription model, it may still be more beneficial than investing in servers and additional hard-to-find IT personnel.
A software as a service model would give machine vision teams the flexibility and ease of investing in a cloud-based platform with a subscription while new features, models, and updates are seamlessly added by the technology partner. Deep learning cloud-based platforms allow for model edge deployment on PCs and other devices to support flexible, digitised workflows on the production line, wherever a user is located.
54% of manufacturing leaders in Europe (61% globally) expect AI to drive growth by 2029, up from 37% (41% globally) in 2024, according to Zebra’s 2024 Manufacturing Vision Study; while 26% (27% globally) believe one of today’s most significant quality management issues is integrating data. With these AI and data goals, the time is ripe to look at the potential of the cloud to leverage data and extend the benefits of deep learning machine vision.
