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Digital Twin Journey: Computer Vision AI Model Enhancement with Dell Technologies Solutions & NVIDIA Omniverse
Introduction Business considerations Solution overview
Solution overview
Characteristics of a Digital Twin
Foundational building blocks of a Digital Twin
Core Digital Twin components Real-world data input Starting point for a Digital Twin journey
Solution
Computer Vision 3D Digital Twin model/simulation framework Synthetic data generation Apply virtual model to real world
Partner overview Solution architecture overview Use case validation Conclusions References

Solution

Home > Workload Solutions > Computer Vision > White Papers > Digital Twin Journey: Computer Vision AI Model Enhancement with Dell Technologies Solutions & NVIDIA Omniverse > Solution

Solution

  • Computer Vision

    Computer Vision (CV) is a field of Artificial Intelligence (AI) that provides computers and systems with the ability to extract relevant information from digital images, videos, and other digital sensor inputs.

    Object detection is a specific application of CV AI technology using algorithms to detect, identify, and track objects in images and videos. It involves finding the coordinates of an object within an image and video, then updating the scene with the specific object enclosed with a bounding box. Typically, CV object detection models are trained on datasets containing sufficient examples of the required object(s) including object labels and coordinates information.

    Figure 2. Example Computer Vision workflow utilizing synthetic data
    This figure shows the object detection process: augment dataset with synthetic data, train the CV object detection model, and model performance.

    This solution leverages 3D modeling, simulation, and synthetic data generation (SDG) technologies to enhance the performance capabilities of a typical CV object detection model.

    Figure 3. Applied Computer Vision Digital Twin workflow
    This flowchart shows the process of Digital Twin development: 3D modeling, simulation, and synthetic data generation (SDG) technologies to enhance the performance capabilities of a typical CV object detection model.

    This paper focuses on the following Digital Twin CV workflow categories (2, 3, 4, and 5 in the previous figure:

    • 2. Build Model
    • 3. Scenario Simulations
    • 4. Synthetic Data Generation
    • 5. Apply to the Real World