Deep learning is rapidly spreading across computer vision applications. The benefits of artificial neural networks (ANNs) are twofold. ANNs have the potential to improve the accuracy and robustness for applications in factory automation, robotics or retail. At the same time, ANNs have the capability to solve image-based application problems that could not be solved in the past, such as pathology detection in microscopy or complex pattern classification in surface detection.
How do apples differ from pears? Up to now, developers have set differentiation criteria. Neural networks learn these criteria on their own. Learn what it takes for them to accurately apply what they’ve learned.
Read White Paper
Thanks to deep learning-based artificial intelligence (AI), there are numerous opportunities in industrial image processing. Learn about the advantages and disadvantages compared to conventional methods.
Read White Paper
Deep learning algorithms enable an increasing amount of new applications that were previously considered unsolvable for machines. Find out about vision systems for deep learning models in Medical & Life Sciences.
Read White Paper
Deep learning increasingly takes over tasks handled by conventional algorithm-based image processing, as this approach yields better image processing results in many applications. For some applications, deep neural networks such as convolutional neural networks (CNN) are particularly well suited.
Read Product Insight
Neural networks such as convolutional neural networks (CNN) on FPGAs take over classical image processing tasks in industry. If these are exclusively, more easily or better solvable with deep learning, it displaces classical image processing – especially in the case of disturbances such as reflective surfaces.
Read Product Insight
Small neural networks are sufficient for many vision applications. Processors such as FPGAs can therefore be used for convolutional neural networks (CNN). This opens up a wide range of applications far beyond classification tasks and also enables use in embedded vision systems.
Read Product Insight
Billions are being invested worldwide in the use of artificial intelligence (AI) in a wide range of social areas. At the same time, laws, standards and ethical guidelines are being enacted to ensure sensible use of this technology – and also to emphasize its benefits to society.
Read Product Insight
Basler has developed an automated retail payment terminal made of embedded technology and artificial intelligence (AI) software. It identifies and classifies products in a shopping cart and displays the prices.
Read Use CaseEmbedded technologies are finding their way into diagnostics and analytics. They are powerful, space-saving and inexpensive. Add an artificial neural network (ANN) and bacteria can be classified in a split second.
Watch Use Case video
Basler and Data Spree have developed an accurate and robust real-time solution for sorting fruit. The vision system is based on a Basler blaze time-of-flight camera and a deep learning platform from Data Spree.
Read Use Case
Do you want to develop an embedded vision sensor to detect and classify objects? Basler has set up a convolutional neural network (CNN) with a deep learning framework and is using this in an embedded device.
Read Use Case
Terms such as machine learning, deep learning or artificial intelligence (AI) are being used in more and more areas of everyday life. What exactly does this mean, how does it all work and what are its application areas?
Read Vision Campus articleHow do you teach a machine to learn autonomously? How does a machine become an “intelligent” machine with the help of neural networks and what can we use this called artificial intelligence (AI) for?
Watch Vision Campus Video
