AI Powered Tools for Autonomous Systems Development

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Deepen AI combines the best of computer vision, deep learning, and sensor fusion.


Our tools are powered by AI algorithms that can improve quality and throughput for scalable production.  Moreover, our tools can process fused sensor data from a variety of sensors including camera, LiDAR and radar, taking what’s best from each sensor type to arrive at highly valuable data sets for the training of perception, prediction, and motion planning models.

2D Data Labeling Tools

Deepen tools support all types of popular image annotation requirements including 2D bounding boxes, lane markings, semantic and instance segmentations, as well as 3D cuboids.  In addition, our tools incorporate advance AI that help pre-populate labels so users only need to perform minimal adjustments for edge cases and QA.

3D Data Labeling Tools

Deepen tools can process large volumes of sequential 3D LiDAR point cloud and 2D image (video) data and labels.  Our labeling solution supports numerous object classes and persistent object IDs that are crucial in model training.  Our fused sensor annotation solution also incorporates advance AI that can significantly reduce human intervention for high quality and scalable production.
Deepen tools can leverage what’s best from each sensor type (camera, LiDAR and radar) to help generate highly valuable data sets for model training.  For example, our tool can fuse color information from camera images and depth information from LiDAR point clouds to help label objects far away from the host.  This can provide additional, if not more accurate, information to your model over basic image labeling solutions.

Visualization, Validation and Benchmarking


In addition to annotation, Deepen tools also provide the proper environment for our users to visualize labels and benchmark against models in development.  Users can upload their own classification or prediction models and very quickly see how their models perform against ground truth data – allowing for fast and iterative model development.


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