Deepen AI Launches Deepen Calibrate

Deepen AI, a world leader in computer vision tools for autonomous systems, has announced the launch of Deepen Calibrate, a unique new software tool that makes the critical task of sensor data calibration simple and quick.

Deepen Calibrate manages the complexities of the calibration process, ensuring accuracy and making autonomous systems safer, while also making a job that typically required the time of a PhD-level engineer into something anyone can do.

Sensor data calibration is critical for self-driving vehicles and other autonomous systems. These systems depend on sets of sensors, including cameras and LiDAR, to “see” the world around them, but these sensors must be carefully calibrated with each other and with reality to ensure the systems work properly and safely. Until now, sensor calibration has been the kind of task that mixed art and science and required the time of the most senior and experienced engineers on any team.

Deepen Calibrate is aimed at enabling users to visualize and inspect data quality integrity for training and validation. Deepen Calibrate can cut the time spent on calibrating multi-sensor data from hours to minutes, massively accelerating computer vision training — and opening the world for more AI technology by democratizing this key part of the data curation.

Key features of Deepen Calibrate include:

Visualize & inspect integrity of multi-sensor data seamlessly
Calculate intrinsic & extrinsic calibration parameters
Export calibrated multi-sensor data into Deepen's annotation tools

Deepen Calibrate extends the company's suite of data lifecycle tools, including Deepen Annotate and Deepen Validate. Deepen Calibrate available to customers starting today.

“Safety has always been our top priority, and the foundation for safety is reliable data,” said Mohammad Musa, CEO & Co-founder of Deepen AI. “With Deepen Calibrate, we empower our customers to visualize and inspect data with a click of a few buttons — while also saving them engineering man-hours — so they can move ahead with their computer vision training datasets with confidence.”