Major features of HALCON 24.11
In this release the users can look forward to groundbreaking technologies and improvements. With HALCON 24.11, we are focusing on even better AI, specifically deep learning algorithms. Among other features, users can now detect and evaluate unexpected behavior in deep-learning-based classification.
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Discover our HALCON Leaflet
In our New-Features-Leaflet you will find all important information about our software HALCON. Discover MVTec HALCON and benefit from new features and short release cycles.
Out of Distribution Detection (OOD) for Classification
This new HALCON feature makes it easy to recognize unexpected behavior caused by incorrect classifications in production. Thus, users can take appropriate measures, such as stopping the machine, in a targeted and efficient manner. When using a deep learning classifier, unknown objects are assigned to one of the classes that the system has learned. This can lead to problems if, for example, the defects or objects themselves are of a type that has never occurred before. The new deep learning feature “Out of Distribution Detection (OOD)” indicates when an object is classified that was not included in the training data. For example, this could be a bottle with a green label if the system was only trained on bottles with red or yellow labels. In such cases, HALCON provides the message “Out of Distribution” together with an OOD score that indicates how much the deviation from the trained classes is.
The OOD score can also be useful when expanding deep learning models with new training images by indicating which of the new images will have the greatest value for the new model. For example, a high OOD score for a new training image indicates a greater deviation from the images already in the network – this means a higher information content and, therefore, greater value for the training.
Preview of the new IDE HDevelopEVO
HALCON 24.11 has a special highlight for all users of HALCON's own integrated development environment (IDE) HDevelop: a preview of the new IDE HDevelopEVO. This is characterized, among other things, by a more modern, intuitive user interface and an improved editor (i.e., the central programming element). The latter enables faster and more efficient programming and prototyping of machine vision applications. Users can already extensively test the new development environment in HALCON 24.11. The range of functions of HDevelopEVO will be continuously expanded in the coming releases and it will over time become the standard HALCON development environment.
Improved Shape-based Matching
The new HALCON version makes the “Shape-based Matching” feature, used in many applications, more user-friendly. This technology is used to find objects fast, accurately, and precisely. HALCON 24.11 includes the new patent pending "Extended Parameter Estimation" for this purpose. This allows parameters to be estimated with greater granularity, which significantly speeds up execution in some applications. “Extended Parameter Estimation” enables this estimation also for users without in-depth machine vision expertise.
Optimized QR code reader
The performance of HALCON's QR Code Reader has been significantly increased. This is particularly evident under difficult conditions, for example, when many codes need to be found in the image area or many textures in the image complicate the detection. The recognition rate has been increased and the evaluation time has been significantly reduced in demanding scenarios.
Deep 3D Matching
With this feature, HALCON 24.11 contains a deep-learning-based market innovation for the 3D vision sector, especially for bin-picking and pick-and-place applications. This feature is particularly robust in determining the exact position and rotation of a trained object and is characterized by very low parameterization effort and fast execution time. Depending on the accuracy requirements, one or more cost-efficient standard 2D cameras can be used to determine the position. Training is performed exclusively on synthetic data generated from a CAD model. Further training is therefore not required.
Customers can already run this feature in HALCON 24.11 – to train the model and evaluate applications, they can contact MVTec at any time. Training and evaluation within HALCON will follow in the next release.
HALCON’s GigE Vision interface supports RoCEv2
With this release, HALCON's GigE Vision interface supports the RoCEv2 network protocol, which enables increased performance in image transmission.
Improved HALCON Progress edition
HALCON Progress is now fully compatible with the HALCON Steady edition. Progress users can now collaborate with Steady users on the same projects. Additionally, HALCON Progress users will receive the same maintenance updates as HALCON Steady users. In the future, switching from Steady to Progress will simply require exchanging the license file.
