Latest Version – HALCON 19.05

The latest HALCON version 19.05 was released in May 2019. Below, you will find a an overview over some of the features included in this release.

Deep Learning Inference on Arm Processors

Deep learning inference

With HALCON 19.05, customers can execute the deep learning inference directly on Arm® processors. This allows them to deploy deep learning applications on embedded devices without the need of any further dedicated hardware. All three deep learning technologies image classification, object detection, and semantic segmentation are supported and run on Arm-based embedded devices out of the box.

Enhanced Object Detection

Object detection

HALCON’s deep-learning-based object detection localizes trained object classes and identifies them with a surrounding rectangle. HALCON 19.05 now also gives users the option to have these rectangles aligned according to the orientation of the object. This results in a more precise detection, as rectangles now match the shape of the object more closely.

Improved Surface-based Matching

Edge-supported surface-based matching is now more robust against noisy point clouds: Users can control the impact of surface and edge information via multiple min-scores. Additionally, in case that no xyz-images are available, a new parameter now allows switching off 3D edge alignment entirely. This enables users to eliminate the influence of insufficient 3D data on matching results, while keeping the valuable 2D information for surface and 2D edge alignment.

Enhanced Shape-based Matching

With HALCON 19.05, users can now specifically define so-called “clutter” regions when using shape-based matching. These are areas within a search model that should not contain any contours. Adding such clutter information to the search model leads to more robust matching results, for example in the context of repetitive structures.

Previous Version – HALCON 18.11

HALCON 18.11 was released in November 2018. It was officially introduced at VISION 2018 and, amongst other things, includes new AI technologies, specifically from the fields of deep learning and Convolutional Neural Networks (CNNs).

HALCON 18.11 is available in two editions: Steady and Progress. While the latter is available as a subscription with a six-month release cycle, the Steady edition – as successor of HALCON 13 – is offered as one-time purchase.


Below you find an overvier over the most prominent features of this release. For a detailed list, please have a look at the release notes.

Deep Learning

Deep Learning in MVTec HALCON

With HALCON 18.11, users are able to train their own classifier using pretrained CNNs (Convolutional Neural Networks) included in HALCON. These networks have been highly optimized for industrial applications and are based on hundreds of thousands of images. HALCON 18.11 offers a seamlessly integrated, comprehensive set of deep learning functions for

  • classifying entire images
  • object detection
  • semantic segmentation.

Learn more about Deep Learning with HALCON on this page.

New Data Structure “Dictionaries”

dictionaries icon

HALCON 18.11 introduces a new data structure “dictionary”, which is an associative array that opens up various new ways to work with complex data.

For example, this allows bundling various complex data types (e.g., an image, cor­re­spond­ing ROIs and parameters) into a single dictionary, making it easier to structure programs when, e.g., passing many parameters to a procedure.

Handle Variable Inspect in HDevelop

With HALCON 18.11, HDevelop can display detailed information on most important handle variables. This allows developers to easily inspect the current properties of complex data structures at a glance, which is extremely useful for debugging.

Find more information on this page.

HALCON in Your Industrial Network

HALCON 18.11 introduces the Hilscher-cifX interface. This allows HALCON to communicate with almost all industrial field bus protocols via Hilscher cards. Among others, CC-Link, EtherCAT, EtherNet/IP, PROFIBUS, and PROFINET are supported.