Newest HALCON Features
On this page, you will find information on the newest features of MVTec's standard machine vision software HALCON.
The latest HALCON version was released in May 2019:
Preview – HALCON 19.11
The next HALCON version 19.11 will be released in November 2019. To sweeten the waiting time, you will find a preview of some features included in this release below.
Deep Learning Anomaly Detection
Automated surface inspection is an important task in many manufacturing industries and deep-learning-based solutions are becoming a standard tool which can be used to distinguish parts, detect and segment defects. However, it is often not easy to get enough images of the defect or the effort of labeling the available data is very high.
HALCON's new Anomaly Detection feature gives you the possibility to perform an inspection using only a relatively low number of "good" images for the training. The inference results in the "anomaly" that was detected in the inspected image compared to the trained images. On the right, you can see an example of a defective bottleneck.
ECC 200 Code Reader Speedup
In HALCON 19.11, the code reader for ECC 200 codes has been significantly accelerated for multi-core systems. The biggest improvement was achieved for codes that are particularly hard to detect and read. For such codes a speedup of about 200% can be achieved. This speedup also greatly increases the viability of embedded-based code readers by making optimum use of existing hardware capacities.
Generic Box Finder
In HALCON 19.11, a new functionality for pick and place applications is available: The generic box finder allows the user to find boxes of different sizes based on 3D space, eliminating the need to train a model for each required box size. This makes many applications much more efficient – especially within the logistics and pharmaceutical industries, where usually boxes of a large variety of different sizes are used.
Many companies work with open source frameworks to train classifiers for deep learning models (CNN). These CNNs can be exported into the ONNX (Open Neural Network Exchange) format. HALCON 19.11 is able to read data in ONNX format, allowing to use previously created 3rd party networks within HALCON.
Latest Version – HALCON 19.05
The latest HALCON version 19.05 was released in May 2019. Below, you will find an overview of some of the features included in this release.
Deep Learning Inference on Arm Processors
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
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.
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.
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.
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.
New Data Structure "Dictionaries"
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.
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