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business objectives, and the reluctance to move forward with AI and machine vision can be at least partially attributed to the levels of complexity of projects that may benefit from it. “Companies are mainly focusing on very
complex projects where it can take a really long time to implement tools such as AI. However, there are new technologies in the field that can make it easier to implement these tools, potentially taking smaller steps to drive adoption of machine machine vision and artificial intelligence,” said Pottel. One such technology is the latest
generation of optical character recognition (OCR) tools. Historically used for automotive tasks such as licence plate recognition, the very latest OCR is equipped with deep learning, which uses a neural network that mimics the human brain to deliver very high accuracy. This has the dual benefit of helping to overcome some of the challenges associated with traditional OCR, such as training time, stability and complexity, and also providing a quick win when it comes to the adoption of AI. Speaking at the webcast, Allan
Anderson, Managing Director at ClearView Imaging and Chairman of UKIVA, said: “Deep learning OCR can be a really good entry point for anyone wanting to deploy AI online. If, as the figures suggest, people are not having much success in actually deploying AI, it could be because they’re trying to tackle
the kind of complex problems which, in time, can be solved. But if they have little experience with AI, it might be better to start smaller, build the knowledge and then go on from there.” This technology is particularly useful
for the more challenging tasks, including reading damaged or blurred text, or dealing with changing lighting conditions and complex backgrounds. It can also deal with issues relating to reflective surfaces such as steel or glass.
AI and OCR solutions Zebra Technologies has just such a tool available, thanks to a rugged OCR tool integrated into its Aurora image processing software. The tool utilises deep learning for greater consistency and accuracy when imaging alphanumeric characters, and it works straight out of the box, so reduces the need for special skills or AI experience. “What users tend to find is that the ‘out
of box’ experience is really good,” said Anderson. “Initially, in a quick stick with a thin plate application, it would have taken weeks of set-up in a lab with conventional tools just to be able to validate with that work, then trials on the line and trying to get things working and maybe not succeeding. “If you take something like a deep
learning OCR tool, pull it out of the box, grab your camera, and start pointing at text, because it’s been trained on such a huge amount of images and lots of
different variations, you’re going to get success.” Looking to the future, as technology
emerges and more challenges are overcome with AI, it could be set to drive growth in the automotive industry, said Anne Wendel, Director Machine Vision at the VDMA Robotics + Automation Association. She told the webcast audience that automotive is an important sector for machine vision and an early adopter of new trends, saying that AI software is expected to become a standard component of MV solutions. “Years ago, AI was a technical curiosity. Today, AI-based solutions can be seen everywhere. From our smartphones, banks, shops, production facilities, logistics centres, AI comes into play and we all use it,” she said. “Now, deep learning AI is revolutionising machine vision and some say, reinventing the art of what is possible.” Wendel highlighted some of the
VDMA’s research monitoring AI in machine vision over the years: “In 2019, many survey participants stated that AI-based solutions had not yet provided acceptance. Looking at the most recent English market survey in 2023, hundreds mentioned AI as a driver of current and future machine vision. “Answers reported demand for AI- based machine vision solutions, and also noted that software-considered-as AI will become a standard crucial part of machine vision systems,” she said. I
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Advancing automotive with AI and machine vision in Germany and the UK
This White Paper explores the potential of machine vision and AI for OEMs in the automotive industry, outlining the latest trends, challenges, and opportunities. It presents findings from a survey commissioned by Zebra Technologies in June 2023, focusing on 500 automotive OEMs, tier 1 and tier 2 suppliers across the UK and Germany.
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DECEMBER 2023/JANUARY 2024 IMAGING AND MACHINE VISION EUROPE 25
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