DEEP LEARNING
Neural networks flood Vision 2018 trade fair
Deep learning was in force at the Vision trade fair in Stuttgart. Greg Blackman reports from the show
M
ore than six million people visit Oktoberfest in Munich each year, drinking around seven million litres
of beer during the 16-day festival. Oktoberfest could be considered one of the defining events for Austrian start-up MoonVision, which put its real-time object tracking technology through its paces at the 2017 festival. Te firm was exhibiting at the Vision show in Stuttgart, and Georg Bartels, the company’s head of sales, told Imaging and Machine Vision Europe that Oktoberfest was a ‘stress test’ for the young company. As with a lot of today’s start-ups using image
analysis, MoonVision’s technology is based on neural networks. Te company was founded in August 2017 and has 12 employees at its Vienna headquarters. German caterer Ammer Wiesn approached
MoonVision to add an automated quality assurance layer to its food service during Oktoberfest, to ensure the food leaving the kitchen tallied with each waiter’s order. MoonVision installed a camera over the exit of the kitchen looking directly down on the waiters and the plates. Te system was trained to detect the 20 or so dishes and the haircuts of the 40 waiters who worked over the 16 days. Te dishes and staff were labelled in the
images and the neural network trained on video recorded on the first day of the festival;
Adaptive Vision’s deep learning software can be used to classify eggs 4 Imaging and Machine Vision Europe • December 2018/January 2019 @imveurope
www.imveurope.com
the system had to be up and running quickly, according to Bartels. Te tracking solution worked at high frame
rates to deal with rapid movement, and had to identify partially hidden dishes, all with limited computing power – the final system required only seven per cent capacity of a modern GPU to deliver information in real time. Bartels said during Vision that the firm was
now getting a lot of interest from automotive companies, and has recently worked with a producer of bearings to automate surface inspection.
Accelerating CNNs MoonVision was one of five start-ups exhibiting on a joint stand for research institutes, universities and start-ups, organised by Messe Stuttgart and VDMA Machine Vision. Te gathering momentum behind artificial intelligence and deep learning is now very much apparent in the machine vision sector, with companies such as MoonVision developing vision solutions based on neural
networks that address wide-ranging imaging tasks. Deevio was another start-up on the joint stand, using deep learning to improve industrial quality control. Most machine vision soſtware library
suppliers now incorporate deep learning tools in their packages, which were on display alongside exhibitors showing dedicated deep learning soſtware, such as South Korean firms Laon People and Sualab. Flir launched its Firefly camera at the show, which incorporates Intel’s Movidius Myriad 2 vision processing unit (VPU) for real-time deep learning inference. Te VPU has hardware accelerators for image processing, and includes streaming hybrid architecture engine processor cores that accelerate on-camera inference based on neural networks. Putting neural network acceleration directly on the camera means inference can be run at ‘the edge’ rather than sending data elsewhere for processing. Te initial version of the Firefly camera uses a 1.6 megapixel Sony Pregius global shutter sensor at 60fps. Flir was able to build Intel’s Movidius chip
Adaptive Vision
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