How Eos helps you perceive more clearly


Eos increases system safety by overcoming the shortcomings of computer vision, especially in harsh lighting and poor weather.

Up to 3x more accurate than any alternative, especially in difficult edge cases.

Improve your system utilization by enabling effective perception 24/7 in any imaging scenario.

Address the robustness limitations of current vision system architectures through an end-to-end deep learning approach.

Eos can quickly support any sensor and lens configuration and addresses condition-related training set bias. This enables you to easily bring up your vision system and also evaluate different sensing options.

Replace the need for intensive training dataset capture and annotation for each configuration.

Remove “good condition” bias through automated dataset enhancements and training methodology for harsh conditions.

Save many months of engineering time and $100s of thousands of dollars per sensor configuration.

Eos deep-learning architecture co-optimizes perception and RAW image processing to provide the highest system performance.

Highly efficient real-time performance across common target processors.

Removes suboptimal lengthy camera tuning for computer vision with end-to-end training specific to your camera.

Eos can support any processor, accelerator, or sensor. It removes the limitation of you being “locked in” to one provider.

Maintain the flexibility to choose what system design is best for you, instead of being locked to specific vendors.

Choose the perception features specific to your application with our modular solution.

Accelerate the integration of perception modules into your system with our collaborative engagement process.


Eos Embedded Perception Software

Built for All Vision Systems

Eos Embedded Perception Software
Built for All Vision Systems 

We built Eos Embedded Perception Software with a highly optimized end-to-end architecture and a novel AI deep learning framework that uniquely addresses the challenges of perception in harsh conditions. 

This reduces model training cost and time by orders of magnitude and removes sensor and processor lock-in, something not possible with today’s deep learning methods.

Camera-Based Perception

Eos addresses individual NCAP requirements, L2+ ADAS, higher levels of autonomy from highway autopilot and autonomous valet (self-parking) to L4 autonomous vehicles as well as Smart City applications such as video security and fleet management.

Multi-Sensor Fusion

Eos provides multi-sensor early fusion for L2+ and higher autonomous vehicles and robots. Combined with stereo or depth-sensing cameras, Eos provides an alternative to Lidar at a fraction of the cost.

End-to-End Perception

Perception software enabling end-to-end learning of computer vision systems. The approach allows customers to easily adapt their existing datasets to new system requirements, enabling reuse and reducing effort and cost vs. existing training methodologies.