EMBEDDED VISION
Edge imaging driven by AI, say Embedded World experts
Greg Blackman reports from the Embedded World show, where a panel gave insights into vision processing at the edge
D
iscussions around embedded vision tend to turn to AI at some point, and the panel session during
the Embedded World digital show was no different. Customers often want to add AI functionality to a vision system, explained Arndt Bake, chief marketing officer at Basler, during the discussion. However, AI doesn’t run well on a PC, so customers are looking for hardware where AI inferences do run well, he said – namely embedded architectures. Bake was joined on the panel by Olaf
Munkelt, managing director of MVTec Software; Fredrik Nilsson, head of the machine vision business unit at Sick; and
Austin Ashe, head of strategic partners and channels, IoT devices at Amazon Web Services (AWS). Te session was organised by VDMA Machine Vision along with Messe Nürnberg. Bake added that ‘customers are struggling
somewhat with the stretch of changing from the classic architecture to the new [embedded] architecture. We need to enable customers to take small steps... to migrate from one technology base to the other.’ Basler is partnering with AWS to provide
vision solutions using the latter’s Lookout for Vision service. It allows customers to train machine learning models in the cloud that can then be deployed on edge devices, such as camera boards from Basler. Lookout for Vision is designed to make
it easier to use machine learning, targeting manufacturing and the industrial internet of things.
Ashe commented during the discussion
that AWS is trying to lower the barrier to entry for customers wanting to use embedded vision for the first time, or those wanting to expand and scale it. He said that 75 per cent
22 IMAGING AND MACHINE VISION EUROPE APRIL/MAY 2021
of businesses plan to move from pilot to full operational implementations of embedded systems over the next two to five years. ‘We are positioning ourselves to
orchestrate the edge and the cloud in a unique way,’ he said, adding that edge processing is important when it comes to latency, bandwidth, cost of sending data, and security and privacy. Te cloud comes into play for things like
monitoring devices, and also updating them. ‘When you think about managing embedded vision systems at scale, there is an elegance that comes with being able to take a [neural network] model, train it in the cloud, and deploy it over the air to all of the machines that need it,’ Ashe said. He added that the adoption of 5G ‘creates a whole new opportunity for cloud and edge to have closer interoperability and more edge- to-cloud use cases to be delivered.’ In terms of using AI, Munkelt and Nilsson
agreed that AI adds value and opens possibilities for using vision, but that it has to be easier to use. ‘We have to enable customers of embedded vision to quickly get
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