Southeast Asia’s Cutting Edge Technology Provider in Machine Vision, Industrial Automation and Industry 4.0

HALCON Deep Learning 20.11

HALCON Deep Learning 20.11

Deep Learning 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.

Industry Applications:

NEWEST HALCON FEATURES

HALCON 20.11

HALCON 20.11 was released in November, 2020, and comes with many new and improved features that help you further enhance your machine vision performance. Next to optimizations for a number of core technologies, this new version also includes a new feature called Deep OCR. With this feature you can profit from a holistic deep-learning-based approach for optical character recognition (OCR).

HALCON 20.11 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 is offered as one-time purchase.

Find more information on this version’s new features below or have a look at the webinar recording of our feature presentation.

Scene with many objects or edges

IMPROVED SURFACE-BASED 3D-MATCHING

In HALCON 20.11, the core technology, edge-supported surface-based 3D-matching, is significantly faster for 3D scenes containing many objects and edges.

In addition to this speedup, the usability has been improved by removing the need to set a viewpoint.

DotCode

DOTCODE AND DATA MATRIX RECTANGULAR EXTENSION

In HALCON 20.11, the data code reader has been extended by the new code type, DotCode. This type of 2D code is based on a matrix of dots. It can be printed very quickly and is especially suitable for high speed manufacturing lines, like those used in the tobacco industry.

Furthermore, the ECC 200 code reader now supports the Data Matrix Rectangular Extension (DMRE).

Good results despite of low contrast

IMPROVED SHAPE-BASED MATCHING

In HALCON 20.11, the core technology, shape-based matching, has been improved.

More parameters are now estimated automatically. This increases usability as well as the matching rate and robustness in low contrast and high noise situations.

Screenshot of HDevelop

HDEVELOP FACELIFT

For enhanced usability, HALCON’s integrated development environment HDevelop has been given a facelift.

In HALCON 20.11, more options for individual configurations have been implemented, featuring e.g., a dark mode and a new modern window docking concept. Moreover, themes are now available to improve visual ergonomics and to suit individual preferences.

MVTec's deep learning edge extraction can distinguish between parquet joints and the wood grain.

DEEP LEARNING EDGE EXTRACTION

Deep learning edge extraction is a new and unique method to robustly extract edges (e.g., object boundaries) that comes with two major use cases.

Especially for scenarios where a variety of edges is visible in an image, it can be trained with only few images to reliably extract the desired edges. Hence, the programming effort to extract specific kinds of edges is highly reduced with this version of MVTec HALCON. Besides, the pretrained network is innately able to robustly detect edges in low contrast and high noise situations. This makes it possible to extract edges that usual edge detection filters cannot detect.

HALCON/PYTHON

HALCON 20.11 introduces a new HALCON/Python interface. This enables developers who work with Python to easily access HALCON’s powerful operator set.