MACHINE VISION
HAVING A VISION FOR AI AND DEEP LEARNING Advances in deep learning/AI is resulting in these technologies being increasingly utilised within machine vision solutions. Control Engineering Europe sought advice about how end users can ensure that they are able to implement successful AI-based machine vision applications
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eil Sandhu, UK product manager for Imaging, Measurement & Ranging at SICK, believes that AI/ deep learning machine vision will result in greater production flexibility because it has the potential to retrain machines, adapt to changes in processes and respond to a high variety of products – all of which are, of course key elements of Industry 4.0. “Deep Learning technologies should be especially attractive to end users because they can cut out tedious and lengthy programming time and costs especially for more complex tasks,” he said. “This offers the potential to automate machine vision tasks that have previously been too difficult, costly or time-consuming.” However, Sandhu goes on to warn that deep learning should not be considered as a silver bullet for every application. He believes that it is suited to harder-to-solve inspections where there are a greater number of natural variations from a standard, which would be laborious or even impossible to solve one at a time. Ruben Ferraz, field product marketing manager Deep Learning at Cognex, pointed out that, as with any new technology there are considerations and trade-offs so the advice is to set proper expectations for what deep learning can bring to any project. “It is important to understand these trade-offs at the outset,” he said. With any deep learning project, there are four core job roles that are needed for resource planning. These include:
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1. A vision developer who implements the solution, as well as optimises lighting and image formation; 2. A quality expert who analyses and grades images; 3. An image labeler; and 4. A data collector who records and organises all information including images, grades, labels, and meta data. While it is possible for one employee to cover more than one of these roles, being aware of the types of expertise needed is helpful to have upfront. It is also worth nothing that any deep learning initiative will require a powerful Windows-based PC with a graphical processing unit (GPU) installed. Ferraz advises that the best route forward is to pilot small manageable projects in a sensible phased approach to allow automation teams to set themselves up for long-term success with deep learning image analysis. “Pick a project with a clear payback that cannot easily be solved with traditional rule-based vision, but which is not so difficult that it never makes it into production. Focus on a core need and develop both a core competency and understanding of what deep learning can and cannot do in a factory automation setting. Deep learning pilot projects should have two primary goals: evaluating its broader utility for a more holistic automation strategy
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and automating an inspection or
verification process that is either not done at all or done manually,” he said.
Change is coming Due to complexity, variability, and the necessity to distinguish between very small differences some inspection applications have been impossible to achieve with traditional machine vision systems. However, things are now changing, according to Damir Dolar, director of embedded engineering at FRAMOS. He said: “Deep learning can Control Engineering Europe
