Perception & ADAS
Object detection, lane-keeping and collision-avoidance models depend on consistent, frame-accurate labels. We keep identities stable across video so tracking stays reliable.
Annotation · Sector
Annotation for autonomous vehicles & mobility.
Perception systems are only as good as the data behind them. We label the road scenes — in image and video, 2D and 3D — that teach vehicles to see other road users, read the road, and understand what's drivable.
The same detection, segmentation and tracking skills we honed in agriculture transfer directly to mobility: keeping object identities consistent across frames, resolving occlusion the same way every time, and holding a single taxonomy across millions of frames. That consistency is exactly what large mobility datasets live or die on.
Annotation types
What the work looks like.
2D detection & segmentation
Camera-image bounding boxes, semantic and instance segmentation, and lane lines with attributes — the backbone of ADAS perception.
3D & sensor fusion
Cuboids on LiDAR point clouds and camera↔LiDAR↔radar fusion, so objects are placed in real-world space, not just the image plane.
Lane & drivable space
Lane-line types, road markings, and free-space / drivable-area segmentation — what the planner needs to decide where the car can go.
Video object tracking
Frame-accurate multi-object tracking that holds identities across a sequence, so temporal models learn motion and intent, not just appearance.
Coverage
What we annotate.
2D & 3D boxes
LiDAR cuboids
Lane & road segmentation
Drivable-space masks
Pedestrians & cyclists
Traffic signs & lights
Video tracking (MOT)
Sensor fusion
Where it's used
How it's used.
Image and video, together
Road scenes rarely live in a single frame. We annotate sequences — not just stills — so temporal models learn motion and intent.
Consistency at scale
Large mobility datasets fail on inconsistency. Per-annotator quality tracking and multi-pass review keep the taxonomy identical across millions of frames.
This is a newer domain for us, built on the same detection and tracking foundations as our agritech work. We'd start with a calibration batch to lock the taxonomy to your stack.
