MVTec
HALCON Progress 26.05
What is HALCON Progress
HALCON Progress is the fast track to the latest features. With new releases approximately every six months, it gives you access to the newest features quicker and more frequently than ever before. HALCON Progress development licenses are exclusively available via an annual subscription. A valid HALCON Progress development license grants access to all Progress releases within the subscription period. For more information about our licensing models, please visit.
HALCON Progress is the fast track to the latest features. With new releases approximately every six months, it gives you access to the newest features quicker and more frequently than ever before. HALCON Progress development licenses are exclusively available via an annual subscription. A valid HALCON Progress development license grants access to all Progress releases within the subscription period. For more information about our licensing models, please visit.
Highlights
- HALCON is your solution for the full range of applications in the field of machine vision
- Enabler of the Industrial Internet of Things (aka Industry 4.0)
- Large imaging library of more than 2,100 operators
- Integrated development environment (IDE) for machine vision: HDevelop
- Huge range of features including deep learning
- Easy programming in C, C++, C#, Python, and Visual Basic .NET
- Available for a multitude of platforms
- Support of multi-core and multiprocessor computers
- High performance through utilization of state-of-the-art instruction sets and GPU Acceleration
- Support of hundreds of industrial cameras, frame grabbers, and all common vision standards
Features
- Revolutionary software for 3D machine vision
- Matching to find even rotated or partly occluded objects
- Blob analysis with more than 50 shape and gray value features
- High-accuracy measuring
- Huge range of latest deep learning technologies
- Optical character recognition and verification (OCR/OCV)
- Arbitrarily shaped regions of interest (ROIs) for significant flexibility and speed
- Detection of lines, circles, and ellipses with an accuracy of up to 1/50 pixel
- Extremely fast morphology
- Color image processing and hyperspectral imaging
- Processing of extremely large images (more than 32k x 32k)
- Image sequence processing (e.g., for surveillance tasks)
- Accurate 3D camera calibration
Newest Features Of HALCON 26.05
HALCON 26.05 has been released on May 20, 2026. This upcoming version introduces major enhancements that improve robustness, speed, and usability across both classical and deep-learning-based machine vision workflows. Key innovations include automated contour optimization for shape-based matching and Data Matrix rectification for reliable code reading on curved or deformed surfaces. In the deep learning domain, enhanced data augmentation and a new generation of object detection deliver improved model performance, faster inference, and reduced dependency on large training datasets, enabling more stable operation in demanding industrial environments.
Automatic Contour Optimization for Shape Matching
Automatically remove unstable edges from SBM models for faster, more robust matching.
With HALCON 26.05, automatic contour optimization for shape-based matching (SBM) enables users to automatically remove unstable or misleading contours from SBM models. Reflections, shadows, or random texture often introduce unreliable contours that reduce matching robustness and require labor-intensive manual cleanup.
With the new feature, users simply provide sample images of real object instances. Based on these samples, the system analyzes which contours appear consistently across variations and retains only the stable contours while removing unreliable ones.
By focusing the model on robust contours only, matching becomes faster, more stable, and more accurate. The feature replaces time-consuming manual contour editing with a data-driven optimization step during model training. It is particularly valuable for reflective or textured objects, such as mechanical or electronic components, and for automation scenarios like feeder-based pick-and-place systems where stable matching is critical. The automated optimization also reduces the effort for retraining models when new objects are introduced, making inline retooling more practical in production environments.
Data Matrix Rectification
Reliable Data Matrix reading on curved or deformed surfaces.
HALCON 26.05 expands its code reader with Data Matrix rectification, enabling reliable reading of Data Matrix codes even when they appear on curved or deformed surfaces. In many industrial applications, codes are printed on non-flat materials, which can distort the symbol geometry and reduce reading reliability. With the new rectification capability, HALCON compensates for these distortions before decoding, significantly improving robustness in such scenarios.
The rectification step can be enabled optionally within the code reader and integrates seamlessly into existing workflows. Although processing time is slightly higher than with standard Data Matrix reading, the improved decoding reliability enables stable operation in demanding environments. Typical applications include codes printed on cylindrical components, curved packaging, or flexible materials used in manufacturing and logistics.
Enhanced Data Augmentation
Flexible augmentation pipelines that improve deep-learning robustness and reduce training data requirements.
HALCON 26.05 introduces enhanced data augmentation for deep-learning workflows. The new approach replaces the previous procedure-based augmentation and preprocessing steps with configurable operators that integrate directly into HALCON deep-learning pipelines.
Users can define augmentation pipelines programmatically, apply transformation techniques such as geometric transforms, color variations, and blurring, and preview the resulting image variations. This lets developers test and refine augmentation strategies quickly within their existing workflow. Training samples generated this way help model robustness and generalization, which can reduce the reliance on large training datasets.
This feature is initially available for object detection and instance segmentation, supporting more reliable training and improving model generalization under challenging conditions such as varying illumination, perspective changes, occlusions, or noisy image data.
Next-Generation AI Object Detection
Next-generation object detection in HALCON with significantly faster inference and high detection accuracy.
A new generation of deep-learning-based object detection is available with HALCON 26.05, delivering up to 5x faster inference while maintaining high detection accuracy. The new architecture enables efficient detection of objects and is optimized for demanding machine vision scenarios where both speed and precision are critical.
Users can train and run detection models directly within HALCON and start from MVTec-provided pretrained models that can be adapted to specific applications. The anchor-free detection approach improves bounding-box localization and performs reliably even for small objects and varying object sizes. Integrated data augmentation techniques further increase robustness against changes in illumination, rotation, distortion, and partial occlusion. The feature integrates directly into HALCON workflows and supports inspection, localization, and sorting tasks across industrial automation applications.
Latest Preview Version Of HDevelopEVO
Stay up to date with the latest developments in HDevelopEVO, the next-generation integrated development environment for HALCON. This version offers advanced tools and an improved workflow designed to enhance the machine vision development process. Get an early look at upcoming features and prepare for future updates to support your ongoing projects and development needs.
Newest features of HALCON 25.11
MVTec HALCON 25.11 has been launched in November 2025. This version again brings some improvements as well as new features, such as Continual Learning – Classification, a new technology that makes training and maintaining classification models faster and more flexible. The new HALCON release also includes Score Visualization for Shape Matching, Optimized Deep OCR models for faster, resource-efficient OCR and further improvements.
Continual Learning – Classification
HALCON 25.11 introduces Continual Learning – Classification, a new technology that makes training and maintaining classification models faster and more flexible. Users can create models with only few images per class and adapt them at any time – for example, to refine existing classes or add new ones.
Score Visualization for Shape Matching
With Score Visualization for Shape Matching in HALCON 25.11, users gain increased transparency when setting up shape matching applications. Instead of only returning an overall score, the feature provides a breakdown of how different model parts contribute to the final result. By configuring color-coded bins, users can immediately see which areas match well and which perform poorly, for example due to shadows or unwanted textures. This visual feedback makes it much easier to refine models, remove problematic parts, and optimize applications – a major usability advantage especially for non-expert users.
The feature can also support advanced scenarios in robotics, helping determine which object in a stack is least covered and should be picked first.
Optimized Deep OCR models for faster, resource-efficient OCR
With new Deep OCR recognition models in HALCON 25.11, text reading becomes faster and more resource-efficient without compromising accuracy. The models deliver up to 50× faster inference on embedded devices.
All models are pretrained by MVTec on industrial image data, and include the proven alignment preprocessing, which improves recognition when text varies in position or orientation. Thanks to their optimized architecture, they enable real-time OCR applications on low-power devices while maintaining high accuracy. This makes the models ideal for demanding inline applications such as serial number inspection, label verification, or lot tracking OCR tasks, across industries from logistics and packaging to pharmaceuticals, consumer goods, and medical technology.
MobileNetV4 classification models
With HALCON 25.11, MVTec adds support for the MobileNetV4 series, an efficient new generation of deep learning models optimized for resource-constrained systems and edge devices. These models support both classification and object detection tasks and deliver high accuracy while maintaining low computational requirements. Users benefit from fast inference times, lower system costs, and straightforward integration into existing HALCON projects. All models are pretrained by MVTec, ensuring strong performance for various downstream tasks such as quality inspection, product classification, presence detection, and surface defect analysis. Typical industries include automation, electronics, packaging, food, and medical technology.
Various code reading and print quality inspection improvements
With HALCON 25.11, code reading and print quality inspection (PQI) become even more robust and versatile. QR code detection has been improved for challenging cases such as curved or deformed surfaces. A more powerful candidate search significantly raises the detection rate, while runtime has been reduced for standard scenarios – enabling reliable reading in industries like logistics, packaging, food production, and bottle labeling. The bar code reader has also been enhanced for Code 128 and GS1-128, making it more tolerant to irregular bar widths caused by printing variations or local distortions. This increases decoding reliability across diverse industrial applications. In addition, HALCON now supports the latest print quality inspection standards ISO/IEC 15415:2024 and ISO/IEC 29158:2025. This ensures code quality can be verified according to the most up-to-date requirements in sectors such as pharmaceuticals, food, and logistics. Together, these enhancements provide compliance, long-term process stability, and higher robustness across a wide range of industrial code reading applications.
Built-in SBOMs for easier compliance
With HALCON 25.11, MVTec provides Software Bills of Materials (SBOMs), giving users transparent insight into the software components included in the product. SBOMs are becoming a key requirement under new regulations such as the EU Cyber Resilience Act and are increasingly demanded in process- and safety-critical industries.By providing SBOMs directly with HALCON, MVTec simplifies compliance and reduces workload for customers.
Delivered as machine-readable SPDX JSON files, SBOMs make it easier to perform vulnerability and license analyses, fulfill regulatory obligations, and react quickly to newly discovered risks. The result is less integration effort, lower long-term costs, and greater confidence in meeting both regulatory and customer requirements.
Latest preview version of HDevelopEVO
Syntax highlighting for HALCON Script files in HDevelopEVO 25.11
HDevelopEVO 25.11 introduces redesigned syntax highlighting for HALCON Script files, making code easier to read, navigate, and maintain. Instead of uniform coloring, operators, variables, and comments are now displayed in distinct colors, giving scripts a clear visual structure. This improves orientation in the code, reduces errors, and speeds up debugging and refactoring – resulting in a more efficient workflow and a smoother development experience.。
HALCON Script Engine and C++ API in HDevelopEV
With HDevelopEVO 25.11, MVTec introduces the first preview of the HALCON Script Engine, the successor to the HDevEngine. It provides a runtime environment for executing HALCON Script files created in HDevelopEVO. The HALCON Script Engine can initially be integrated into applications via a C++ API. Further interfaces such as .NET and Python are planned for future releases. This bridges the gap between prototyping in HDevelopEVO and productive use in custom solutions. As a preview version, the HALCON Script Engine already enables embedding HALCON Scripts into applications. While not all language features are supported yet, these will follow in future releases. In the meantime, users can try it out and gain early experience with the new workflow.
Additional features
Also included in this release are several improvements that make working with HDevelopEVO more efficient. A new script converter simplifies the migration of existing HDevelop procedures and example programs into HDevelopEVO, supporting stepwise conversion and reuse of established code. Usability has been enhanced with interactive tools: a real-time histogram integrated into the threshold operator for intuitive parameter adjustment, and a live display of grayscale values on mouse hover for instant pixel-level analysis. Together, these features simplify migration, speed up troubleshooting, and streamline everyday image processing workflows.
AGRICULTURE & FOOD
Identification of natural products,automated fruit picking and sorting, or fill level measurement: HALCON is a technology for producers and packagers alike to achieve efficient and consistent production and keep up with the ever-changing demands of consumers.
AUTOMOTIVE & ROBOTICS
Determine the 3D pose of objects, extract 3D data for bin picking or robot path planning: HALCON's unique 3D vision techniques open new possibilities for numerous automotive and robotics applications.
LOGISTICS & PACKAGING
Quality control, completeness inspection, identification, or bar & data code reading: HALCON offers outstanding methods in all areas of logistics and packaging.
ELECTRONICS & SEMICONDUCTORS
Precise assembly, surface inspection or defect detection during the entire manufacturing process: With HALCON, system manufacturers are fully equipped to implement advanced processes at reduced costs.
HALCON Editions
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