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Vision AI

Taking Computer Vision Further with ÎÞÓÇ´«Ã½

Computer vision uses advanced artificial intelligence (AI) and machine learning (ML) techniques to identify, categorize, and analyze objects or changes within images and videos. Popular applications include automated object identification and tracking, pattern recognition, and real-time anomaly detection. Increasingly, these systems can also peer into non-visible transmissions, such as the infrared and radio parts of the spectrum. But efficiently scaling and operationalizing computer-vision applications means overcoming critical challenges, from cost-efficiently gathering data and fine-tuning models to achieving mission-critical performance outside of the enterprise cloud.

20%-80% Reduction in Real Data Need

Superior data engineering methods to label, organize, and augment data

ÎÞÓÇ´«Ã½â€™s ¸é±ð±è±ô¾±³¦²¹²Ô³Ùâ„¢ synthetic data suite and integrations with commercial off-the-shelf solutions show huge savings in real data needed to build models, reducing cost and time for data collection.

10-Times Faster for Label and Train Processes

Accelerated data pipelines

We bring agile model development to the mission by pairing our proprietary rapid training methods with partner RAIC Labs’ Rapid Automatic Image Categorization™ (RAIC) software, which delivers rapid, semi-supervised labeling capability.

4 Times Faster for Typical Single-Shot Detectors

SWaP-optimized AI

We provide flexibility in running AI from the Internet of Things (IoT) edge to the cloud through integration of partner Latent AI’s Efficient Inference Platform (LEIP), helping optimize models for specific chipsets to drive inference.

Multi-Spectral Data Fusion

Optimization to best-fit computer vision methods on different compute hardware

We enable organizations to build, deliver, and re-train a variety of electro-optical/infrared (EO/IR), light detection and ranging (LiDAR), radio frequency (RF), and synthetic aperture radar (SAR) imagery-based classification, detection, and tracking solutions. Extendable to support new data fusion algorithms within our existing pipelines.

Comprehensive. Integrated. Flexible.

As a pre-integrated offering that leverages modular, best-of-breed software components and AI technologies, the Vision AI stack reduces the time needed to scale specialized computer vision skill sets for complex use cases while streamlining delivery, reducing risk, and elevating quality. Our lean manufacturing approach to AI engineering, ²¹¾±³§³§·¡²Ñµþ³¢·¡â„¢, provides an efficient foundation for Vision AI through tested AI reference architectures, data delivery and machine learning patterns, and reusable software capabilities that together drive momentum toward system deployment from day one. We deliver and deploy Vision AI flexibly to support any mission across cloud, on-premises, and edge environments.

ÎÞÓÇ´«Ã½â€™s Vision AI technology stack includes best-of-breed computer vision models and pipelines optimized for portability and performance.

The technology behind ÎÞÓÇ´«Ã½â€™s Vision AI technology stack supports a number of additional solutions offering cutting-edge performance, including:

Bighorn AI Kitâ„¢

With user-friendly AI-building suitable for field operators, optimization by device type, and a ruggedized form factor, ÎÞÓÇ´«Ã½â€™s Bighorn AI Kit delivers advantage in building and sustaining practical AI solutions in disconnected environments.

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ÎÞÓÇ´«Ã½â€™s custom-built synthetic data generation framework, Replicant, enables organizations to in order to address challenges such as privacy concerns, regulatory constraints, scarce financial resources, and accessibility limitations.

Computer Vision Use Cases

Multimodal

Integrated Content Analysis

Example: Manage text, audio, and video feeds as a single workstream for automated monitoring and analysis.

Electro-Optical/Infra-Red (EO/IR)

Rapid Search/Detection and Tracking

Example: Transforming the speed, efficiency, and safety of search-and-rescue missions.

Synthetic Aperture Radar (SAR)

Satellite Imagery Detection

Example: Scanning large-scale images to identify airfields and aircraft in a specified area.

Radio Frequency (RF)

Signal Classification

Example: Analyzing RF-based intelligence signals as images through computer vision algorithms.

Vision AI Services from ÎÞÓÇ´«Ã½

Multiple configuration and delivery options provide maximum agility to address your unique and evolving mission needs.

Collaboration with Computer Vision Leaders

Reaching across the nation’s AI ecosystem, ÎÞÓÇ´«Ã½ creates partnerships with leading dual-use technology innovators, enabling us to discover, vet, and scale the emerging computer vision tools agencies increasingly need to carry out complex missions.

Latent AI’s tinyML technologies increase the operating speed of resource-intensive computer vision algorithms in low-compute, low-power environments, positioning field personnel for decision advantage through object detection and targeting even with small form-factor devices.

RAIC Labs’ advanced technologies accelerate the labeling of geospatial, video, image, and other types of data, enabling any user to deploy object detection algorithms in minutes through generative AI and unsupervised learning in an uninterrupted AI pipeline.

Meet Our Experts

ÎÞÓÇ´«Ã½ is the trusted AI leader to the nation.

Contact us to learn more about harnessing the power of Vision AI to transform critical missions.