Zebra Technologies Corporation, the digital solution provider, will demonstrate end-to-end AI machine vision solutions at VISION 2024 from 8-10 October in Stuttgart, Germany.
Visitors will see how manufacturers, including those in the automotive, food and beverage, and packaging sectors can equip their engineers, programmers and data scientists with flexible deep learning machine vision solutions on devices, PCs, at line side and across multiple factory sites.
“The latest and best deep learning machine vision systems also need the best implementation options for ease of use,” said Donato Montanari, Vice President and General Manager, Machine Vision, Zebra Technologies. “Manufacturers can develop more connected factories and collaborative teams with deep learning, from ready out-of-the-box tools for the non-specialist to advanced toolsets for data scientists.”
Zebra’s 2024 Manufacturing Vision Study shows that 54% of manufacturers in Europe (61% globally) expect AI to drive growth by 2029. Thirty-six percent are using deep learning for process automation today, with 63% planning to implement it in the next five years.
Zebra’s booth will feature more than a dozen machine vision hardware and software demonstrations, highlighting the flexibility and capabilities of its portfolio. Attendees will have the opportunity to learn about and interact with Zebra’s Aurora software suite.
Other demos include PC-based and FS42 camera-based deep learning optical character recognition, deep learning anomaly detection on a smart vision sensor, and an Aurora Vision Studio conveyor demo. There will also be 3D sensing demos, using Zebra’s 3S Series 3D sensors and AltiZ devices with Aurora software for 3D inspection.
Zebra’s machine vision leaders will share video updates from its booth at VISION, which can be viewed on LinkedIn here at 16.00 CET (10.00 EST).
During VISION, Montanari will deliver a presentation titled, “The Cloud Will Deliver End-To-End AI Solutions for Machine Vison Users” about the need for machine vision teams to consider how the cloud can elevate and improve today’s deep learning machine vision.
“A cloud approach would enable a rich and diverse data pool from across sites that could be labelled and annotated, and deep leaning models trained, with users having access to new collaboration tools and the latest computing resources,” said Montanari. “Such a platform opens up true end-to-end AI solutions with model edge deployment on PCs and devices to support flexible, digitised workflows on the production line, on a PC or device wherever a user or team is located.”
