Advanced Computer Vision Enables Customers to See Solutions More Clearly

A field of artificial intelligence (AI), computer vision is a science that focuses on training and enabling computers to interpret and understand the visual world. Computers use digital images from cameras and videos and deep learning models to identify and classify objects, and use that “knowledge” to accurately identify and classify objects and react to what they “see” – whether people, things, or other kinds of information.

For more than two decades, Cybernet has been developing and advancing our work in computer vision capabilities and have produced proven solutions for automated recognitions of targets, missiles in flight, air/spacecraft outlines, and physical road features, for example.

In our portfolio of solutions, our systems recognize an ever-growing list of objects from one or several cameras, with object descriptions input through commonly used CAD formats like IGES or DXF — spacecrafts, parts, grasp points, docking targets, or anything that can be defined by a CAD drawing or description.

When presented with workspace views, including objects and other features such as background clutter, etc., Cybernet’s vision systems fixate only on objects that have been “taught” to the machine. Once the target is matched, based on the computer’s learned “knowledge,” the object’s position and orientation is calculated.

Vision Systems for Automation

Cybernet also develops computer vision systems capable of reconstructing the 3D structure of an object from a series of 2D images. The 3D information can then be used to determine the object type, position, and orientation. Given enough images, this information provides the necessary data to estimate, for example, a missile’s trajectory, or allow an autonomously running vehicle to follow the road safely.

Cybernet’s extensive research and production of automation solutions has allowed for deeper development of computer vision-based technology that automates rendezvous and docking of air/spacecraft to support unmanned resupply efforts.

This technology seeks to enable autonomous Unmanned Aerial Vehicle (UAV) refueling, effectively extending their maximum mission length. Cybernet’s approach matches target air/spacecraft images with pre-stored, 3D geometry representing the target air/spacecraft, in order to determine misalignment errors in six degrees of freedom in real time, and correctly insert refueling probes without requiring active human operation.

How Machine Vision and Computer Vision Work Together

Before there was computer vision, there was machine vision. Machine vision uses cameras, processing hardware and software to automate both mundane and complex inspection tasks and accurately guide handling equipment during product assembly. It enables reliable and fast 24/7 inspections and help to improve the efficiency of manufacturing operations, resulting in improved product quality, higher yields, and lower production costs.

Computer vision can be used alone, without needing to be part of a larger machine system. But a machine vision system doesn’t work without a computer and specific software at its core.

3D Reconstruction from Handheld
Sensor Tracking
Fuze Arming State Identification

Making the Future Possible

Faster and simpler process

More accurate outcomes

Cost reduction

Can be used across industries

Data mining

Making the Future Possible

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