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Newest HALCON Features

HALCON 21.11 - The latest Version

Below, you can see an overview of the major features included in HALCON 21.11, which was released in November 2021.

HALCON 21.11 Instance segmentation
HALCON 21.11: Highly accurate identification and measurement of bacteria in petri dish

Deep Learning Instance Segmentation

With HALCON 21.11 MVTec extends the functional scope of its deep learning features with a new technology called “instance segmentation”. This combines the advantages of semantic segmentation and object detection. With the help of instance segmentation, objects can be assigned to different classes with pixel accuracy. This technology is particularly useful in applications where objects are very close to each other, touch or overlap. Typical use cases also include grabbing randomly arranged objects from boxes (bin picking) as well as identifying and measuring naturally grown structures.

Improved Bar Code Reader for Code 128 

With HALCON 21.11, HALCON’s bar code reader is improved in terms of robustness in case of blurred Code 128/GS1-128 codes. Now, codes with a larger amount of blur can be read. Blur on such codes can occur due to motion or due to limitations in depth of focus. The Code 128/GS1-128 is a widely used bar code type that is frequently used in logistics due to its compact size and high data density. 

Improved Dictionary Handling

HALCON 21.11 Improved Dictionary Handling

Dictionaries make it easy and convenient to manage complex data in HALCON. For example, different data types such as images, ROIs and parameter settings can be bundled in a single dictionary. This allows programs to be structured in a logical way, for example when passing many parameters to a procedure.

HALCON 21.11 includes several improvements that make the handling of dictionaries even easier and faster. For example, dictionaries can now be initialized with a single operator call, and the syntax for adding and retrieving elements has been simplified. In addition, the auto-completion now also suggests the keys contained in the dictionary, which further speeds up and simplifies working with dictionaries.


Future-Proof Interface for Shape Matching

HALCON 21.11 Future-Proof Interface for Shape Matching

With Generic Shape Matching, HALCON offers user-friendly access to MVTec's industry-proven shape matching technologies. Thanks to the significant reduction in the number of required operators, users can implement solutions more easily and quickly. 

With HALCON 21.11, existing functionalities are enhanced based on customer feedback to further increase usability. For example, the clutter feature has been integrated, handle inspection has been optimized, and additional parameters have been integrated and included in the automatic parameter estimation.

Previous Progress and Steady Versions

Download HALCON 20.11

Previous Features

Have a look at the features from previous releases. All features listed below are available for HALCON Progress or Steady users with a version 20.11 and higher.

HALCON 21.05

Features included in this version:


  • Deep OCR improvements
  • Generic Shape Matching
  • HDevelop usability improvements
  • HALCON Deep Learning Framework
  • Subpixel bar code reader improvements

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HALCON 20.11

Features included in this version:


  • Improved Surface-based 3D-Matching

  • DotCode and Data Matrix Rectangular Extension

  • Deep OCR

  • Improved Shape-based Matching

  • HDevelop Facelift

  • Deep Learning Edge Extraction

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HALCON 20.05

Features included in this version:


  • Subpixel Bar Code Reader
  • More robust Surface-Based 3D Matching

  • Deep Learning Training on CPU

  • Further Improvements

    • More robust Generic Box Finder

    • Anomaly Detection Improvements

    • CPU Support for Grad-CAM-based Heatmap

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HALCON 19.11

Features included in this version:


  • Deep Learning Anomaly Detection
  • ECC 200 Code Reader Speedup

  • Generic Box Finder

  • ONNX-Support

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HALCON 19.05

Features included in this version:


  • Deep Learning Inference on Arm Processors
  • Enhanced Object Detection

  • Improved Surface-based Matching

  • Enhanced Shape-based Matching

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