Artificial intelligence (AI) plays a key role in the digital age. Self-learning algorithms have the potential to improve processes and products in the field of machine vision and to secure competitive advantages. They are equally suited to automation and logistics as they are to monitoring products and processing goods. This is also due to the fact that classical image processing solutions work with a fixed set of rules, making organic or fast-changing objects a major challenge. Artificial intelligence, on the other hand, can handle such cases effortlessly. So where are the challenges to technology? And what needs to be done to make it widely accepted?
The hurdle for the application of AI-based image processing solutions is still quite high. They usually require expertise, programming efforts and investment in computer and storage hardware. Not only training a neural network, but also using it and evaluating the results require knowledge of hardware, software and interfaces. This poses challenges for many companies. IDS shows that it can be done differently: The IDS NXT AI vision system (www.ids-nxt.com) comes with all the necessary tools and workflows, allowing users to easily build intelligent vision solutions.
Using the IDS NXT lighthouse Cloud software, even users with no prior knowledge of artificial intelligence or camera programming can train a neural network. Since it is a web application, all functions and the necessary infrastructure are immediately available. The engineer or programmer does not need to set up his own development environment, but can immediately start training his own neural network. Three basic steps are required for this: Uploading sample images, labelling the images and starting the automatic training. The generated network can be run directly on the IDS NXT industrial cameras, which are capable of delivering the desired information or passing commands to machines via REST or OPC UA.
Deep Learning: a game changer in automation
Artificial intelligence is penetrating new areas with incredible speed and enabling applications where classic image processing is too expensive, inflexible and also too complex. Dr Alexander Windberger, AI specialist at IDS Imaging Development GmbH, explains: “Not only has the game changed, but also the players. AI-based image processing works differently from classic rule-based image processing, because the quality of the results is no longer just the product of manually developed program code, but is primarily determined by the quality of the data sets used”.
Thus, users need different core competences to work with AI vision. The approach and processing of vision tasks is therefore changing. Domain experts are coming into the picture, as they can use their knowledge to keep the creation of data value going and react flexibly to shifts in data and concepts during operations.
However, many companies still have reservations about the new technology. There is a lack of expertise and time to familiarise themselves with the subject in detail. At the same time, the Vision community is growing from the IoT sector and the start-up scene. With the new application areas and user groups, there are inevitably other use cases and requirements. The classic programming SDK is no longer sufficient to provide the best possible support for the entire workflow, from the creation of datasets and training to the implementation of a neural network.
Looking to the future, Dr Windberger states: “We realise that entirely new tools are emerging today for working with AI vision, which are used by very heterogeneous user groups without AI and programming knowledge. This improves the usability of the tools and lowers the barrier to entry, which is currently significantly accelerating the spread of AI-based image processing. Ultimately, AI is a tool for humans and so it must be intuitive and efficient to use.”
Driving force behind current developments
No other component collects and interprets as much data as image processing. Industrial cameras enable what is seen, such as product characteristics (e.g. length, distance, number), states (“presence/absence”) or quality, to be monitored in the production process, processed and the results passed on to the value-added systems in the network.
This not only determines whether the inspected part meets the desired characteristics or is good or bad, but can also trigger an intelligent action, such as automated sorting, depending on the result. Especially small and medium-sized enterprises, for which the automation of their production could not be realised competitively due to a lower number of pieces in production, benefit from this plus in flexibility.
Factory automation with smart cameras therefore also offers the possibility of reacting more flexibly to difficult market conditions while guaranteeing consistently high production quality and efficiency. Companies for whom the leap to end-to-end digitisation and automation is too great can make significant progress thanks to AI-based image processing. Holistic, user-friendly systems such as IDS NXT pave the way for this.