DEEP LEARNING
Flir’s Firefly camera incorporates Intel’s Movidius Myriad 2 vision processing unit for real-time deep learning inference
people are moving the screw under the camera, it has to recognise hands. Speaking during the VDMA’s series of
industrial vision presentations, Michał Czardybon, general manager of Adaptive Vision, noted that ‘really difficult defects on the surface of parts can be detected reliably’. Te soſtware is able to pick out small scratches on the surface of the screw that are highlighted, even while the screw is being moved around. ‘With traditional tools you would need many
weeks of development, and very complicated algorithms to detect those defects,’ he continued. ‘With deep learning, you just need a couple of minutes to prepare data and a couple of minutes for training.’ Adaptive Vision’s neural networks have
into an industrial vision camera because of the product volumes Flir commands across its portfolio. Face detection was one Firefly demonstration Flir showed at the trade fair.
Learning phase Deep learning has a lot of potential for image analysis, but it’s still largely untested in an industrial environment. Soſtware company MVTec organised a series of deep learning seminars during the second half of 2018 – one was at Vision Stuttgart – the feedback from which was: ‘We are all in a learning phase’, according to the firm’s managing director, Dr Olaf Munkelt. Munkelt was speaking on a VDMA-organised panel discussion at the show. Munkelt commented: ‘We can achieve
remarkable results using deep learning, because we have the computational power available. Tis adds opportunities.’ But he went on to say that deep learning is not the Holy Grail, observing that given a choice between showing a neural network 100,000 images of a screw or measuring the screw with five lines of code, then writing code will yield a result much faster and with less investment. ‘We have to find metrics where we can apply
this technology [deep learning],’ Munkelt said. ‘It has great advantages, but we have to explore more to understand more.’ Tese opinions were echoed by Carsten
Strampe, the MD of Imago Technologies. Strampe told Imaging and Machine Vision Europe that what’s missing is confirmation from end-users about whether deep learning is reliable for industrial inspection. He said
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that this is the next step in the evolution of the technology, at least for industrial vision. Imago Technologies was showing
its VisionBox industrial PC, which incorporates GPUs for tasks such as deep learning computation, while MVTec had demonstrations based on Halcon 18.11, which includes new deep learning functions. In terms of processing power, neural
networks can be very compute intensive. FPGA and computer hardware provider, Xilinx, had a number of vision demonstrations at the show, and was exhibiting off the back of unveiling its Versal adaptive compute acceleration platform (ACAP) chip in October. Versal is a low-power chip that contains an AI processing block. Te integrated circuit is based on 7nm node technology, an upgrade compared to the 16nm node on which Xilinx based most of its prior chip technology. Dale Hitt, director of strategic market development at Xilinx, told Imaging and Machine Vision Europe that Versal will be launched in 2019 for big data uses, while a low- power version for embedded processing will be available in 2020.
Emphasis on training Deep learning is only as accurate as the images with which the neural network is trained. Soſtware firm Adaptive Vision was demonstrating its deep learning tools on the trade show floor, inspecting screws for defects. For its trade fair demo, the company trained its feature detection tool with images of screws with hands in the scene, because if the classifier is to work on live images at a trade fair, where
been optimised for industrial inspection, and use a pre-trained method with the customer only requiring 20 to 50 images to fine-tune the network for a particular application. Te company recommends a GPU to run
its deep learning add-on; it has an execution time of around 100ms for a 1-megapixel image. Adaptive Vision’s four tools based on deep learning are: feature detection, which is very accurate, but the system needs to know
We are all in a learning phase … We have to find metrics where we can apply deep learning
the defect beforehand; anomaly detection, which can be thought of as a golden template- type tool; instance segmentation can identify complicated shapes; and object classification is able to recognise objects such as food. Elsewhere, IDS was releasing new NXT
smart cameras that could accommodate pre- trained neural networks, while Israeli start-up Inspekto was showing how its deep learning technology could help ease vision system integration on the factory floor. Te deep learning systems from MoonVision
and other start-ups inject new technology into the reasonably mature industrial vision sector. Munkelt commented during the panel discussion: ‘Tis technology [deep learning] opens up a lot of opportunities for new companies. Tis brings movement into the [machine vision] industry. And we need some movement; we need new ideas and we need a push forward in order to provide better technology to our customers.’ O
December 2018/January 2019 • Imaging and Machine Vision Europe 5
Flir
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