Description

LumiScan AI Object Matching uses neural networks to detect trained objects in images and reports their position and orientation in pixel coordinates.

The application runs on ctrlX OS (ctrlX CORE or ctrlX IPC) or on a PC (Windows 11 or Ubuntu 24.04), and is controlled through the Data Layer: the ctrlX Data Layer on ctrlX OS, LumiScan Keryx on a PC. It is configured using application and model configuration files. On ctrlX OS it is installed through the App Store.

What is the same for every LumiScan app - how addresses are built, the handshake, execute and stream call patterns, status codes, settings and tools - is described in the LumiScan Keryx manual. This manual covers only what is specific to LumiScan AI Object Matching; the pages of this manual describe its own nodes.

Unlike rule-based image processing, AI Object Matching relies on a trained neural network. The model and its accompanying description file are created with the LumiScan Training Framework and uploaded to the application together.

Where the Images Come From

The application does not acquire images directly from a camera. It reads image data published by another application through the Data Layer.

For example, images can be provided by the LumiScan Cam Driver:

hdvisionsystems/lumiscan-cam-driver/camera1/output/image

The configured image source must provide the image data and the required image information in the expected format.

Models

A trained model defines which objects the application can detect. Models can be created using the LumiScan Annotation Framework and LumiScan Training Framework.

Each model consists of the neural network file and its corresponding description file. These files belong together and should always be uploaded and handled as one unit.

The model configuration defines parameters such as the neural network path, detection thresholds, and inference provider. See Model.json File.

Modules

A module represents one configured instance of AI Object Matching. It defines the image source, the model configuration to use, and where the corresponding results and controls are available in the Data Layer.

Each module therefore combines:

  • An input image source

  • A trained neural network model

  • Detection settings

  • Data Layer controls and results

Currently, only one module can be active at a time.