SICK AppSpace Artificial Intelligence
Deep Learning

Your Benefits

  • Fast, automatic, reliable decision-making by sensors, even for complex tasks
  • Reduces development time and expense: image analyses are trained using example images
  • No additional hardware and software is required thanks to the cloud training
  • Quick support by SICK experts via the SICK Support Portal
  • Training neural networks does not require that the user have sound knowledge of machine learning
  • A free trial offer makes it possible for you to evaluate whether Deep Learning is suitable for your application
  • With SICK AppSpace, it can also be used for very individualized applications


Deep Learning from SICK breaks new ground in industrial automation. With its user-friendly operation, Deep Learning makes it possible to train artificial neural networks for SICK sensors in the cloud with little effort using example images. The sensors can then evaluate and sort objects in line with customized criteria on-site – in machines or systems – even if the natural appearance of the objects varies.

At a glance
  • Interference takes place directly on programmable SICK products
  • Training of neural networks in the SICK cloud
  • Support via SICK Support Portal
  • Neural networks optimized for SICK products
  • Free trial offer on request
  • Deep Learning is part of the SICK AppSpace eco-system


Artificial Intelligence Teaser Box Image
Artificial Intelligence Teaser Box Image

Industrial Artificial Intelligence

Unlocking the full potential of Intelligent Sensors with deep learning solutions

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Supported Products

Product families


Technical overview

  • Technical data overview

    Technical data overview


    Training of neural networks for classifying images

    Classification of images using a neural network previously created with the SICK product dStudio

    TechnologyClassification of images based on artificial neural networks
    Supported products








    Supported browsersGoogle Chrome (version 80 or higher)
All technical data can be found accompanying the individual product