Introduction to the Developer Guide

This section is designed to help you fully understand and use AI Vision Toolkit for OpenVINO(AIVT-OV) Kit. Whether you are first contact LabVIEW This chapter will provide you with step-by-step instructions to help you build quickly. AI Application.

🎯 Objectives of this chapter

Through this chapter, you will learn:

  • How to Configure the Development Environment and Create a New Project

  • How to use AIVT-OV Toolkit for image processing and model inference

  • How to load a custom model and test it

  • How to View Function Descriptions and Usage Examples

  • How to locate and solve common problems in development

  • How to Package and Deploy Projects to Target Devices

👥 Applicable Readers

  • Beginners : Yes LabVIEW and AI I'm not familiar with the toolkit yet. I hope to get started quickly.

  • Developer : Mastering LabVIEW, Planned Access AI Model

  • System Integration Engineer : Wants to build an industrial deployment-level AI Vision system

  • Teachers and researchers: used for teaching demonstration, scientific research experiment, student training and other scenes

🧭 recommend Learning Path

For beginners:

  1. Read the installation guide, complete .vip Kit Installation

  2. Open LabVIEW, run Quick Start Examples (e. g. image acquisition and display)

  3. Attempt target detection task(YOLO model)

  4. View FAQ And common error processing methods

  5. Try loading your own model for a replacement run

  6. Learn how to deploy a project EXE File

📍 Reference section:Quick Start,Example Guide

For advanced users:

  1. Familiar with the three modules:opencv_yiku,OpenVINO,ModelZoo

  2. Call inference function, support ONNX / IR / Paddle Model

  3. Multi-camera acquisition, image segmentation, OCR complex tasks such as identification

  4. Use License Manager implements deployment-side activation

  5. Learn how to optimize model performance and speed (deployment chapter)

🔧 Introduction to Toolkit Structure

AIVT-OV After the kit is installed, LabVIEW Three main modules appear in the function panel:

  • opencv_yikuTraditional Image Processing + Camera acquisition + Model pre-processing and other functions

  • OpenVINO: Support for multiple model formats (ONNX / IR / Paddle) inference interface

  • ModelZoo: Built-in inference module, fast call YOLO / DeeplabV3+ / SAM Other popular models

functions

Details of each module can be found in:Function Module Details

💡 Tips

  • All model paths pleaseAvoid using Chinese paths or spaces

  • Use ONNX The model can be first netron.app View input dimensions and names

🛠 Sample program entry

You can open the sample from the following path: >Help → Find Examples → Directory Structure → VIRobotics → AI Vision find_examples

📚 Extended Reading recommend

Modulerecommend chapter
Installation ConfigurationInstallation Guide
Model inferenceQuick Start,examples and applications
Module DescriptionFunction Module Details
Project DeploymentDeployment and Distribution
Error troubleshootingTroubleshooting,FAQ

Technical Support

If you encounter problems during use, please refer to the relevant chapters or contact technical support: