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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 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).
Deep OCR is a holistic deep-learning-based approach for OCR. This new technology brings machine vision one step closer to human reading.
Compared to existing algorithms, Deep OCR can localize characters much more robustly, regardless of their orientation, font type and polarity. The ability to automatically group characters allows the identification of whole words. This strongly increases the recognition performance since, e.g., misinterpretation of characters with similar appearances can be avoided.
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.
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.
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 20.11 introduces a new HALCON/Python interface. This enables developers who work with Python to easily access HALCON's powerful operator set.
The following features are now also available for HALCON Steady users upgrading from version 18.11.
HALCON 20.05 Features
Subpixel Bar Code Reader
The bar code reader has been improved by an advanced decoding algorithm. Thanks to this, the bar code reader in HALCON 20.05 is even capable of reading codes with an element size smaller than 1 pixel.
More robust Surface-Based 3D Matching
With HALCON 20.05, surface-based 3D matching is more robust in case of almost symmetric objects.
Especially in the assembly industry, workpieces must be located robustly and accurately to allow for further processing. Often, properties like small holes are the only unique feature to find the correct orientation of the object.
HALCON's surface-based 3D matching can now make use of these features to increase accuracy and robustness of the matching result.
Deep Learning Training on CPU
With HALCON 20.05, training for all deep learning technologies can be performed on the CPU. By removing the need for a dedicated GPU, standard industrial PCs (that could not house powerful GPUs) can now be used for training as well. This greatly increases customers' flexibility in implementing deep learning, because training can now be performed directly on the production line, making it possible to adjust the application to changing external conditions "on the fly".
More robust Generic Box Finder
The generic box finder, which was released with HALCON 19.11, allows users to locate boxes of different sizes within a predefined range of height, width, and depth, removing the need to train a model. With HALCON 20.05, it was improved in terms of robustness, performance, speed, and usability. Now, it is much easier to find a wide range of different sizes of various boxes in a robust way.
Anomaly Detection Improvements
The anomaly detection significantly facilitates the automated surface inspection by only requiring a low number of high quality "good" images for training. With HALCON 20.05, training a network for anomaly detection is now up to 10 times faster. Combined with an also faster inference, this opens up entirely new possibilities for trying deep learning on new and existing applications: Training a new network can now mostly be done in a matter of seconds, allowing users to perform many iterations to fine-tune their application without sacrificing a lot of precious time. Trained networks now also require less memory and disk space, which makes HALCON's anomaly detection more viable for the use on embedded devices.
CPU Support for Grad-CAM-based Heatmap
The Grad-CAM-based heatmap (Gradient-weighted Class Activation Mapping) supports you in analyzing which parts of an image influence the classification decision. In HALCON 20.05, the heatmap calculation can also be performed on the CPU. Since this can be done without significant speed drops, customers are now able to analyze their deep learning network's class prediction "on the fly".
HALCON 19.11 Features
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.
HALCON 19.05 Features
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.