HAVING A VISION FOR AI AND DEEP LEARNING

摘要

Advances in deep learning/Al 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 Al-based machine vision applications. Neil Sandhu, UK product manager for Imaging, Measurement & Ranging at SICK, believes that Al/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.