Newest HALCON Features
On this page, you will find information on the newest features of MVTec's standard machine vision software HALCON.
Upcoming Version – HALCON 18.11
In November this year, the next HALCON release HALCON 18.11 will arrive. It will be officially introduced at VISION 2018 and, amongst other things, will include new AI technologies, specifically from the fields of deep learning and Convolutional Neural Networks (CNNs).
HALCON 18.11 will be 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 for regular purchase.
On this page, we will grant you a sneak peek at some of HALCON 18.11's features here – so be sure to check back regularly!
To celebrate the release of HALCON 18.11, we will run an upgrade campaign between November 30 and December 17, 2018.
During the campaign, customers receive a 20% discount on all HALCON 18.11 Steady SDK products, as well as a 20% discount on the first year of all new HALCON Progress subscriptions. If customers purchase a HALCON 18.11 Steady SDK or SDK upgrade AND simultaneously purchase a new HALCON Progress subscription, they will even receive a 50% discount on the first year of these new HALCON Progress subscriptions.
Semantic Segmentation & Object Detection with Deep Learning
With HALCON 18.11, object- or error classes trained with deep learning can now be segmented pixel-precisely.
Combined with the multitude of possibilities that HALCON offers for further processing extracted regions, this semantic segmentation paves the way for an entirely new range of applications, which previously could not be realized, or only with significant programming effort. For example: recognizing objects with a very heterogeneous texture (e.g., plants) or differentiating between different textures in an image, e.g., (un)treated wood, metal or stone.
HALCON 18.11 also introduces deep-learning-based object detection, which allows customers to localize trained object- or error classes in an image.
In contrast to semantic segmentation, objects are marked by a surrounding rectangle (bounding box). The object detection also separates instances of the same class, even if the objects touch each other or partially overlap. This is especially useful when the exact amount of objects is needed, e.g., when checking pill bags for correct filling.
To maximize its potential in industrial environments, HALCON’s semantic segmentation and object detection inference can both be performed on GPUs, as well as CPUs. For both approaches, MVTec provides pretrained networks highly optimized for industrial machine vision applications based on millions of images. These networks make it easier to train new objects by reducing the amount of training images customers have to provide themselves.
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.
Dictionaries can also be read from and written to a file. This allows an engineer to bundle all information necessary to reproduce a certain application's state (e.g., camera calibration settings, defective images, and machine parameters) into a single file. This file can then easily be shared with an machine vision expert for offline-debugging.
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. Double-clicking a handle variable now returns all parameters associated with the handle and their current settings. For example, the user can now easily examine parameters of a data code handle, such as "polarity", "symbol type" or "finder pattern tolerance", as well as complex parameters that carry multiple key-value pairs, like for example the camera parameter of a 3D shape model handle.
ECC 200 Code Reader Improvements
With HALCON 18.11, the data code reader for ECC 200 codes has been improved. The overall recognition rate could be increased by 5 % (data based on our internal ECC 200 benchmark consisting of more than 3,700 images from various applications). In addition, the ECC 200 reader is able to read codes with disturbed quiet zone now. Moreover, codes against complex backgrounds can be found and read faster and more robustly.
HALCON in Your Industrial Network
HALCON 18.11 introduces a Hilscher CifX interface. This allows HALCON to communicate with almost all industrial field bus protocols. PROFIBUS, PROFINET, and Ethernet/IP, among others, are supported.
HALCON 18.05 – Current Version
The latest HALCON version was released on May 22, 2018 and – reflecting the release date – it is named: HALCON 18.05. Below, you can find the major features included in this version.
With HALCON 18.05, customers are able to perform deep learning inference on a CPU
This CPU inference has been highly optimized for Intel®-compatible x86 CPUs. In tests, this resulted in a typical inference execution time on a standard Intel CPU (8 threads) that achieves performance similar to a midrange GPU.
Removing the need for a dedicated GPU greatly increases the operational flexibility. E.g., industrial PCs that usually are not designed for housing large and powerful GPUs can now easily be used for deep-learning-powered classification (inference).
Improved Bar Code Reader
HALCON 18.05 features optimized edge detection, which improves the ability to reliably read bar codes with very small line widths as well as strongly blurred codes. Moreover, the quality of the bar codes is also verified in accordance with the most recent version of the ISO/IEC 15416 standard.
The deflectometry functionality introduced in HALCON 17.12 now includes a new pattern type that improves the precision and robustness of error detection especially on partially specular surfaces like varnished metal sheets.
HALCON 18.05 offers optimized functions for surface-based 3D matching. These can be used to determine the position of objects in 3D space more reliably, making development of 3D applications easier. In addition, HALCON now also includes a new helper procedure that allows developers to quickly inspect and debug parameters and results of a surface-based matching application.
Automatic Handle Clearing
HALCON 18.05 also makes it much more comfortable to work with handles by clearing these automatically once they are no longer required. This significantly reduces the risk of creating memory leaks and makes writing "safe code" much simpler.
Support for Hypercentric Lenses
A new camera model within HALCON now allows the corrections of distortions in images that were recorded with hypercentric (also known as pericentric) camera lenses. These lenses can depict several sides of an object simultaneously, thus enabling a convergent view of the test object. With this technology, users only need a single camera system for inspection and identification tasks, e.g., the inspection of cylindrical objects.
The HDevelop library export feature has been expanded: Developers can now access HDevelop procedures not just in C++, but also in .NET via an exported wrapper – as easily and intuitively as a native function. This significantly facilitates the development process.
Previous Version – HALCON 17.12
HALCON 17.12 was the first release of the HALCON Progress Edition. To learn more about the different HALCON editions, please click here.
Deep Learning out of the Box
With HALCON 17.12, users are able to train their own classifier using CNNs (Convolutional Neural Networks) with HALCON. After training the CNN, it can also be used for classifying new data with HALCON.
Click here to learn more about training and using the CNN.
Inspecting Specular Surfaces with Deflectometry
HALCON 17.12 includes new operators, which enable the user to inspect specular and partially specular surfaces to detect defects by applying the principle of deflectometry. This method uses the reflections on specular objects' surfaces by observing mirror images of known patterns and their deformations on the surface.
Automatic Text Reader
HALCON 17.12 features an improved version of the automatic text reader, which now detects and separates touching characters more robustly.
Surface Fusion For Multiple 3D Point Clouds
HALCON now offers a method that fuses multiple 3D point clouds into one watertight surface. This new method is able to combine data from various 3D sensors, even from different types like a stereo camera, a time of flight camera, and fringe projection. This technology is especially useful for reverse engineering.
With the new HDevelop library export included in HALCON 17.12, calling HDevelop procedures from C++ is as easy and intuitive as calling any other C++ function. This new library export also generates CMake projects.
Previous Version – HALCON 13
To find out more about the numerous features and improvements of our previous version HALCON 13, please click here.
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