HALCON 21.11 provides a wide range of new and improved machine vision features:
AI Accelerator Interface (AI²)
A generic, documented interface similar to MVTec’s Acquisition Interface which allows to implement "Plug-ins" that can be used with MVTec products. This generic interface provides full flexibility with deep learning hardware. So far, TensorRT and OpenVINO toolkits are available, with more to follow.
OpenVINO toolkit plugin
OpenVINO is a toolkit for high-performance deep learning on Intel hardware. The toolkit extends workloads across Intel hardware (including accelerators) and maximises performance. OpenVINO itself provides plugins for certain Intel hardware such as Intel CPUs, Intel GPUs, Intel VPUs (e.g., Intel Movidius Neural Compute Stick). HALCON 21.11 supports the OpenVINO toolkit to speed up the inference. Using the OpenVINO toolkit plugin can drastically shorten the runtime and lower the memory consumption which in turn offers more flexibility in terms of the hardware.
Code128 reader improvements
With HALCON 21.11, HALCON’s bar code reader is improved in terms of robustness in case of blurred Code 128/GS1-128 codes. Now, codes with a larger amount of blur can be read. Blur on such codes can occur due to motion or due to limitations in depth of focus. The Code 128/GS1-128 is a widely used bar code type that is frequently used in logistics due to its compact size and high data density.
Deep Learning Instance Segmentation
With HALCON 21.11 MVTec extends the functional scope of its deep learning features with a new technology called “instance segmentation”. This combines the advantages of semantic segmentation and object detection. With the help of instance segmentation, objects can be assigned to different classes with pixel accuracy. This technology is particularly useful in applications where objects are very close to each other, touch or overlap. Typical use cases also include grabbing randomly arranged objects from boxes (bin picking) as well as identifying and measuring naturally grown structures.
Simplified syntax for dictionaries (HDict)
Dictionaries make it easy and convenient to manage complex data in HALCON. For example, different data types such as images, ROIs and parameter settings can be bundled in a single dictionary. This allows programs to be structured in a logical way, for example when passing many parameters to a procedure. HALCON 21.11 includes several improvements that make the handling of dictionaries even easier and faster. For example, dictionaries can now be initialised with a single operator call, and the syntax for adding and retrieving elements has been simplified. In addition, the auto-completion now also suggests the keys contained in the dictionary, which further speeds up and simplifies working with dictionaries.
Future-proof interface for shape matching
With Generic Shape Matching, HALCON offers user-friendly access to MVTec's industry-proven shape matching technologies. Thanks to the significant reduction in the number of required operators, users can implement solutions more easily and quickly. With HALCON 21.11, existing functionalities are enhanced based on customer feedback to further increase usability. For example, the clutter feature has been integrated, handle inspection has been optimised, and additional parameters have been integrated and included in the automatic parameter estimation.