Artificial Intelligence (AI) is playing an increasingly prominent role in industrial automation and the quality assurance of production processes. At times, it seems as though AI is regarded as the solution to nearly every task in consumer technology. These days, new devices or software are expected to feature at least some form of integrated AI. Despite a degree of hype, driven by promising possibilities and results, it is clear that AI is not a passing trend. AI's capacity to perform complex tasks with high precision makes it extremely valuable to businesses. Ongoing development, fuelled by research investment and funding programmes, suggests that AI is set to become indispensable. The same holds true for Vision AI.
Machine learning specifically unlocks entirely new possibilities that conventional, rule-based image processing simply cannot offer. Nevertheless, it is important to assess the technology realistically and critically in order to understand its true applications and limitations. It is evident that AI will have a lasting impact, with the ability to accurately carry out complex tasks across many fields of application.
How Vision AI works in industrial applications
In industrial applications, AI-based image processing primarily relies on machine learning methods. These methods enable computer programs to learn from experience and automatically extract patterns and insights from data, without the need for explicit programming. This is achieved by adjusting algorithms and models based on data, allowing them to be used for predictions, pattern recognition and decision-making.
AI methods demonstrate their strength particularly with image data that contains highly variable content. They can recognise patterns and features that would be difficult to define with rule-based image processing, such as recurring shapes, colours or positions. The object features relevant for recognition are no longer specified by a predefined set of programs. Neural networks are trained during a learning phase to associate these features with labels by being repeatedly exposed to 'visible' examples. This often requires a large number of diverse sample images, which makes the machine learning algorithms more robust in recognising relevant features, even in unfamiliar environments.
The approaches and methods used in Vision AI
Traditional rule-based image processing algorithms are often designed specifically for a single task and are difficult to adapt to new ones. In contrast, AI offers the ability to train models using 'transfer learning' for one task and then apply them to similar tasks without full retraining. This facilitates the reuse of models and accelerates the development of new machine vision applications.
The quality of results in AI no longer depends on code manually written by an image processing specialist. Instead, this quality is determined by the learning process using suitable sample data. While this process requires a thorough understanding of the application, domain experts who are well acquainted with the products and their specific characteristics can carry out feasibility studies using the right Vision AI tools. As a result, companies are less reliant on programmers and image processing experts during the evaluation phase.
AI systems have the ability to classify images into different categories, which is useful for applications such as image recognition or the identification and classification of products. In addition, AI-based image processing automates many tasks that were previously performed manually, such as detecting defects or sorting objects on conveyor belts. Its ability to identify complex patterns and structures in images, even when these are difficult for the human eye to see, makes it a valuable tool in quality assurance. Overall, the integration of AI-based image processing into these industries leads to improvements in efficiency, quality, safety and cost-effectiveness.
AI Vision plays a crucial role in supporting quality assurance
AI-based methods take a fundamentally different approach, enabling manufacturers such as IDS to create new and intuitive development tools for image processing. These make it possible to translate human quality requirements into image processing systems. The previously complex development of embedded systems, which required specific expertise, benefits considerably from this. The IDS NXT AI camera system demonstrates how easily vision processes can be developed and implemented on a compact, standalone system, without the need for a PC. The fact that much of the development and evaluation process takes place through a simple and intuitive cloud service, without specialised knowledge of AI, application programming or image processing, makes embedded vision accessible to a wider audience.
AI-based algorithms can be executed efficiently in parallel and accelerated with suitable hardware to process large volumes of data in real time. This does not only happen in powerful GPUs housed in large data centres. With the emergence of dedicated NPUs, Vision AI can now also be run in a highly energy-efficient manner on small embedded vision devices. This enables scalable use of the technology, depending on the application requirements across different hardware platforms.
AI Vision makes a significant contribution to the industry's sustainability goals
With the ability to monitor and control production processes, companies can use resources such as water, raw materials and energy far more efficiently. This helps to reduce waste and scrap, which in turn saves resources and energy. By improving the efficiency, quality and sustainability of production processes, AI-driven industrial cameras can help to minimise the environmental impact of the industry while increasing economic profitability.
The limits of AI Vision
It is difficult to pinpoint the exact limits of a technology, especially as it is still very much in development and experience is lacking. The limitations of AI-based image processing become apparent, for example, when the intended results are not achieved. This is not necessarily due to technological shortcomings, but often stems from a lack of experience with AI methods. The greatest challenge, and at the same time the greatest potential for errors, compared with rule-based methods, lies in providing sufficiently good and relevant training data for the learning process. The quality of the input is crucial to the output. An AI system depends on data from which it can learn 'correct behaviour'. If a Vision AI is built under controlled conditions using data that is not representative of later applications, or worse still, if the data contains patterns that reflect biases, the system will adopt these biases and may make biased decisions during use. It is therefore essential to handle the selection and quality of training data with great care.
Industrial AI projects bring various challenges
It is essential to realise that AI is not magic and is currently not yet intelligent enough to anticipate our expectations. AI-based image processing is a powerful tool when applied correctly. To achieve this, it is crucial to define precisely what the task of a machine vision system should be. The clearer the demand for a specific result, the more precisely the appropriate training data can be prepared for the learning process. One challenge lies in attributing undesirable results to the learning process, where the emphasis is on transferring knowledge rather than attempting to steer the decision-making process precisely, as was required in rule-based systems. Inference, often regarded as a "black box", is in fact merely the result of the right input data. The better the system is trained, the more likely the desired outcome. This requires some rethinking of traditional approaches.
Vision AI, or artificial intelligence applied to image processing
There is no universal technology suitable for all applications. It is essential to carry out a thorough analysis to determine which approach is best suited to the specific circumstances. In certain situations, rule-based approaches can still be effective and efficient. On the other hand, machine learning's ability to perform complex tasks with high precision is extraordinarily valuable. However, it is not inherently superior and will not completely replace rule-based image processing. For optimal results, it is often wise to combine several approaches. Those who wish to make successful use of AI must be prepared to experiment and remain open to new ideas and approaches. An innovative mindset will pay dividends in the long term in terms of return on investment.