The CVEDIA team supports industrial partners in testing, training, and validating their systems. CVEDIA is an industry leader in the use of custom synthetic data to develop new sensor technology. Our systems have been precisely built to minimize the gap between real and synthetic data for fluid algorithm training.

CVEDIA simulation tools are designed from the ground up to support and accelerate development from the earliest to the latest stages in the development process. Our SynCity simulation platform provides broadly representative data for new projects, and is used in data intensive classifier training to augment real world data, fill in gaps in coverage, and control for sampling bias. The result is rapidly produced, higher quality training sets that dramatically reduce field data collection, data storage, and labelling expenses.

Use Case

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We recently worked with a client who wanted to create a semi-autonomous airplane cargo loader. Human error when backing up causes cargo vehicles to crash into airplanes, costing airlines millions of dollars a year. A solution would be a vehicle that could be driven near the airplane, and then autonomously parked.

An instant problem was that they were unable to find a manufacturer or airline that would allow them to test (and potentially damage) an airplane. They acquired an airplane fuselage to test on, but it was unsuccessful in mimicking a functioning airplane cassis.

Working with CVEDIA from the design phase out gave the client the ability to create multiple training sets to test the best location and orientation for sensors. They were also able to play with the environment in SynCity to see how sensor conditions and lighting edge cases affected algorithms.

Creating effective real world datasets is a feat in itself, but combining that with the design phase – figuring out the best sensor models, positioning, and placement, can make the project out of reach in time, cost, and effort. CVEDIA synthetic data allows for unlimited possibilities in a fraction of the time.

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Synthetic Datasets Train your machine learning model on validated synthetic data
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SynCity Use SynCity to develop, train, and validate your computer vision system with custom simulations
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Algorithm Training CVEDIA Algorithm Training Services
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Benefits
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    Reduce total project expense
    • SynCity reduces or eliminates the need for sensor deployment and field data
    • CVEDIA synthetic data dramatically reduces data collection and storage expenses
    • SynCity retrieves metadata and annotation from synthetic data - an expense you can absolve from your project
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    Minimize project length
    • Training autonomous applications on synthetic data dramatically reduces time requirements for gathering and organizing field data
    • Datasets can be created and exported in minutes in the browser with SynCity
    • CVEDIA uses assisted algorithm training by choosing which algorithms to apply to most quickly and completely validate your machine learning system
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    Validate your system effectively
    • Account for unmet field data scenarios that may be too difficult, expensive, or dangerous to explore, and simulate fog, sun glare, dust, sand, and water on your sensors
    • Choose from thousands of 3D models (or work with us to create your own) including city infrastructure, natural elements, and humans to ensure your system behaves correctly during unexpected or edge case scenarios

Working with CVEDIA

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CVEDIA projects are developed by an experienced in-house team led by synthetic data industry leaders Arjan Wijnveer and Rodrigo Orph. Our team has been carefully vetted for over 10 years and includes AI veterans with backgrounds in machine learning R&D and large scale deployment.

CVEDIA works iteratively on client projects until satisfactory results are met. Our team has been met with praise from previous and ongoing clients across a range of industries, and we’re happy to be backed by FLIR Systems, the world’s leading thermal sensor producer. CVEDIA works to create custom environments and tool systems for each project, with varying levels of in-house service dependent on client needs.