Release of the latest version 26.05 of the image processing software 'HALCON'
LINX Corporation released version 26.05 of MVTec Software's 'HALCON' on May 20, 2026. Key features include improved AI object detection, enhanced data augmentation, and a preview of the next-gen development environment, 'HDevelopEVO'.
📋 Article Processing Timeline
- 📰 Published: May 21, 2026 at 00:30
- 🔍 Collected: May 20, 2026 at 16:02
- 🤖 AI Analyzed: May 20, 2026 at 18:15 (2h 12m after Collected)
LINX Corporation announced the release of version 26.05 of the 'HALCON Progress Edition' image processing software, developed by Germany's MVTec Software, on May 20, 2026.
HALCON 26.05 features significant improvements in AI-based object detection performance and operational efficiency. Enhancements were also made to shape-based matching and code reading functions.
[Advanced AI Object Detection]
HALCON's AI object detection now features the latest high-performance models. It enables fast and accurate detection even in situations with complex backgrounds, target variations, or occlusions.
[Data Augmentation for Efficient AI Training]
The data augmentation feature, which artificially generates training images, has been improved to enable efficient AI learning even when large datasets are unavailable.
[Automatic Optimization of Shape-based Matching Models]
A new feature has been implemented to identify common features across multiple images and objects, automatically registering stable edges to ensure consistent matching despite individual variations.
[Enhanced Code and Text Reading Performance]
Improvements include better reading of curved 2D codes, automated encoding, and more human-like character recognition.
[New Development Environment: HDevelopEVO]
A preview version of the modern 'HDevelopEVO' development environment has been implemented. While the official release is scheduled for November 2026, users can experience its development efficiency with this version.
HALCON 26.05 is available at no additional cost for annual contract holders. A one-month free trial license is available for new users.
HALCON 26.05 features significant improvements in AI-based object detection performance and operational efficiency. Enhancements were also made to shape-based matching and code reading functions.
[Advanced AI Object Detection]
HALCON's AI object detection now features the latest high-performance models. It enables fast and accurate detection even in situations with complex backgrounds, target variations, or occlusions.
[Data Augmentation for Efficient AI Training]
The data augmentation feature, which artificially generates training images, has been improved to enable efficient AI learning even when large datasets are unavailable.
[Automatic Optimization of Shape-based Matching Models]
A new feature has been implemented to identify common features across multiple images and objects, automatically registering stable edges to ensure consistent matching despite individual variations.
[Enhanced Code and Text Reading Performance]
Improvements include better reading of curved 2D codes, automated encoding, and more human-like character recognition.
[New Development Environment: HDevelopEVO]
A preview version of the modern 'HDevelopEVO' development environment has been implemented. While the official release is scheduled for November 2026, users can experience its development efficiency with this version.
HALCON 26.05 is available at no additional cost for annual contract holders. A one-month free trial license is available for new users.
FAQ
How has AI object detection improved in HALCON 26.05?
Equipped with the latest high-performance models, it enables faster and more accurate detection even in complex backgrounds or with occluded objects.
How does the data augmentation feature help AI training?
It automatically generates more training images internally, enabling efficient learning even when sufficient data is unavailable.
What are the benefits of using HDevelopEVO?
It features a modern UI, AI integration capabilities, and LLM-assisted development features, which increase the efficiency of developing image processing algorithms.