NEW FEATURES
HALCON 26.11 expands its AI object detection with oriented bounding boxes, enabling more precise localization of rotated, elongated, or closely spaced objects. By representing an object’s orientation in addition to its position and size, oriented bounding boxes reduce irrelevant background and enable orientation-aware evaluation.
Per-class confidence thresholds provide further control by allowing users to adjust the balance between detection rates and false positives individually for each object class. For edge deployment, the new ONNX Runtime AI² interface enables more efficient deep learning inference on Linux-based edge devices using the 64 bit Arm architecture.
HALCON 26.11 extends automatic contour optimization to slightly deformable objects. This makes it easier to create robust Shape Matching models for real-world parts that slightly differ in every image. For example, metal or plastic components may vary slightly because of production tolerances, perspective effects, or minor deformations.
Previously, such variations could limit automatic contour optimization and require users to disable it, manually edit model contours, or carefully select training images with minimal geometric differences. HALCON 26.11 can now account for expected local shape variations while identifying and removing genuinely unstable contours. Using multiple representative training images, users can create compact and robust models with less manual contour editing and apply automatic contour optimization to a broader range of real-world matching tasks.
HALCON 26.11 simplifies common 3D vision tasks and delivers more reliable results from real-world data. When fitting geometric shapes, users can now incorporate known object properties such as size or orientation. This helps reduce ambiguities, for example when objects are only partially visible or measurement data contains noise.
Additional improvements make everyday 3D processing workflows easier to handle. A new operator simplifies the calculation of intersections between lines and planes. HALCON 26.11 also preserves neighborhood information when sampling XYZ-mapped 3D data, provides more consistent handling of XYZ mappings when object models are merged, and gives users greater control over surface-normal calculation.
Cybersecurity and regulatory compliance are increasingly important throughout the lifecycle of connected products. With HALCON 26.11, MVTec strengthens the processes supporting vulnerability handling and security updates. Together with the Software Bills of Materials already provided for HALCON, these measures give customers transparent information about included software components and a clearer basis for addressing security issues. This helps manufacturers assess dependencies, maintain their products, and prepare relevant documentation with less effort. HALCON thus supports manufacturers in addressing EU Cyber Resilience Act requirements, while responsibility for assessing the compliance of the complete final product remains with its manufacturer.
