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Harnessing automotive potential with machine vision and AI
A look at the key drivers for the adoption of AI-powered machine vision in the automotive industry, particularly in the UK and Germany
A
I is becoming an integral feature of standard machine vision systems, delivering improved quality, greater
reliability, increased safety and cost effectiveness – factors that are critical to the success of the automotive industry. This is against a backdrop of significant industry challenges, largely caused by increased demand, and exacerbated by difficulties, particularly in supply chains. Even as we move forward from the impact of Covid-19, these supply chains are not as stable as they used to be. Disruptions such as the Suez Canal incident have led to a re-evaluation of manufacturing practices. This, in turn, has given rise to trends including nearshoring, where production is moved closer to the markets. Alternatively, retrofitting existing installations is being increasingly used as a more cost-effective alternative to building new factories. To speed up these processes, automotive original equipment manufacturers (OEMs) can turn to the latest technology, specifically machine vision combined with artificial intelligence (AI). This combination is gaining adoption, but there’s still a way to go. The challenge now is the sheer volume of data generated by these processes, leading to questions about how to handle this data and how to prepare for increasing competition, especially from companies in different regions. Machine vision combined with AI is becoming critical in understanding and combating these challenges.
AI and machine vision in the UK and Germany In the UK and Germany, automotive OEMs and their suppliers are currently predominantly using machine vision for quality assurance and end-of-line detection, alongside other traceability and measurement tasks. This is according to new research commissioned by Zebra
Technologies and carried out by research firm Consensus, which interviewed 500 C+ level directors at machine vision users across different levels in the UK and Germany. A particularly interesting finding
surrounded the incorporation of AI with machine vision. Stephan Pottel, EMEA Practice Lead – Manufacturing, Transportation & Logistics at Zebra Technologies, explained at a recent webcast held in association with Imaging and Machine Vision Europe. He told the audience: “When we asked
a little more in detail about plans to implement AI combined with machine machine vision, this is where you see a lot of differences between the two markets.” Among these differences, some 56%
of UK respondents said that they are currently using some form of AI in their machine vision projects, against 43% of German respondents who answered the same. One of the biggest and somewhat surprising discrepancies came when asked about the current level of AI usage for machine vision projects. The highest response in the UK was for the option “I am using and satisfied with performance” at 38%, whereas in Germany, the most responses were for the “not using, and don’t see use/relevance” option (34%, compared with 24% in the UK). Given
24 IMAGING AND MACHINE VISION EUROPE DECEMBER 2023/JANUARY 2024
that the automotive industry has been known to be an early adopter of new technologies, and Germany as a user of leading practices and procedures, it raises the question: why is there reluctance when it comes to AI? Interestingly, a global State of AI report from Deloitte, looking at the adoption of AI in Germany across a number of industries reached a similar conclusion. Among the reasons cited for this, the report says German respondents reported challenges as more complex than their global counterparts. These include managing AI-related risks; proving the business value and choosing the right AI technologies. Zebra Technologies’ study also highlighted that there is still a way to go in both markets before the potential of AI can be fully realised. Said Pottel: “We have 40-60% that are not using AI yet, and only a third of these companies are planning to use it. That means there are a lot of companies that have no vision at the moment as to how they can increasingly use AI in combination with or in the area of machine vision.”
The complexity challenge At the same time, there is also a feeling of pressure to improve processes in order to keep up with competition and meet
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