MVTec Software GmbH
 

Deep Learning Methods

Deep Learning is a powerful technology that complements MVTec’s comprehensive machine vision toolkit. Customers can develop holistic applications using deep learning in combination with conventional machine vision.

Deep Learning Takes Data Classification to the Next Level

The machine vision software MVTec HALCON comes with pre-trained CNNs (Convolutional Neural Networks), that allow applications to be developed with a relatively small number of training images. By using these networks, users can train their own classifier to classify new data. Provided with a sufficient amount of images, the training process automatically extracts each class’ distinct features. HALCON then analyzes these images and automatically learns which features can be used to identify the given classes.

Advantages of Deep Learning

  • Automatic feature extraction
  • Reduced effort of programming
  • Big amounts of data can be used
  • Reduced development time

Deep Learning Methods in HALCON

What class does the image belong to?

Open Classification

Which pixel belongs to which class?

Open Segmentation

Where is which class located in the image?

Open Object Detection

Is there any difference to the known data?

Open Anomaly Detection

The training for these deep learning methods can be performed on GPUs, as well as on CPUs. This greatly increases your flexibility in implementing deep learning, because training can also be performed directly on the production line. This makes it possible to adjust the application to changing conditions of the environment. The inference can be performed on GPUs, on x86 CPUs and on Arm® processors.

Why use HALCON and not Open Source?

  • Industry proven, pre-trained networks
  • Guaranteed copyright free trained and industry-relevant images
  • The pretrained networks are optimized for typical use-cases regarding speed, performance and complexity
  • Free professional support from our experts worldwide
  • Fast deployment of hotfixes
  • Maintenance and backward compatibility within different versions