Baywatch
Vehicle damage detection at frame level — 95%+ model precision.
7 damage categories · 98% annotation accuracy
Read case →Industries · Autonomous Vehicles
Autonomous-vehicle annotation built for the edge cases that kill models.
AV failures cluster in ambiguity: poor visibility, dense traffic, rare behaviours, weather. We build annotation workflows that hold consistency across the edge cases that matter — the QA spine that sustained 18 months zero-drift on autonomous-robotics workloads.
02 · Data Enrichment
Camera-LiDAR fusion at 99.4% accuracy on 3D annotation.
Multi-pass review across 2D video, LiDAR point clouds, and multi-sensor fusion. Bounding boxes, polygons, semantic segmentation, object tracking, 3D cuboids, calibration-aware workflows. Edge cases mined and annotated: construction zones, dense traffic, rare behaviours, weather extremes. Volume from 500-frame pilots to multi-million-frame annual programmes.
500M+ datapoints · 99.4% accuracy · 18 months zero-drift
2D video annotation
Bounding boxes, polygons, semantic and instance segmentation across multi-frame sequences. Lane tracking, behaviour labelling, occlusion handling.
3D LiDAR point clouds
3D cuboids, point segmentation, ground-plane and obstacle labelling. Sensor sweeps reconciled across temporal frames and multi-sensor alignment.
Sensor fusion and calibration
Camera-LiDAR alignment, multi-modal association, calibration-aware workflows. Synchronised labels across modalities for cleaner training signals.
Edge-case mining and annotation
Construction zones, dense traffic, rare behaviours, weather conditions, low-light scenarios. Mined and annotated to your taxonomy — where models actually fail.
03 · Modality depth
2D video annotation
Bounding boxes, polygons, semantic and instance segmentation across multi-frame sequences. Lane tracking, behaviour labelling, occlusion handling.
3D LiDAR point clouds
3D cuboids, point segmentation, ground-plane and obstacle labelling. Sensor sweeps reconciled across temporal frames and multi-sensor alignment.
Sensor fusion and calibration
Camera-LiDAR alignment, multi-modal association, calibration-aware workflows. Synchronised labels across modalities for cleaner training signals.
Edge-case mining and annotation
Construction zones, dense traffic, rare behaviours, weather conditions, low-light scenarios. Mined and annotated to your taxonomy — where models actually fail.
04 · Customer proof
Programmes that ran this playbook.
05 · Frequently asked · FAQPage schema · 5 pairs
Frequently asked.
How do you annotate camera + LiDAR fusion?
Camera-LiDAR alignment is a standard offering. We use calibration-aware workflows so labels stay synchronised across modalities and temporal frames. Multi-sensor sweeps reconciled per frame; 3D cuboids and 2D projections cross-checked for agreement. Less reconciliation downstream means cleaner training signals.
What's your approach to edge cases?
AV failures cluster in the ambiguous: poor visibility, dense traffic, construction zones, rare behaviours, weather. We mine edge cases from your raw footage and annotate them to your taxonomy with the same QA cadence as the bulk dataset. Edge-case rate per batch is tracked as a delivery KPI.
How do you handle behaviour and trajectory annotation?
Pedestrian, cyclist, and vehicle intent labelled frame-by-frame with multi-frame continuity. Object tracks held across occlusions. Trajectory annotation maps observed motion to predictive labels for downstream prediction model training. Behaviour taxonomies co-developed with your ML team.
Do you have a public AV case study?
Public AV case studies are restricted by client NDAs in this sector. Our autonomous-grade work appears in adjacent published form — Baywatch (automotive video annotation, 95%+ model precision) and the Machani Robotics programme (18 months zero-drift, 99.4% accuracy on 3D annotation). We can walk through an NDA-protected reference call on request.
What does an AV audit cover?
A free 48-hour audit on 100 frames in your modality — bounding boxes, polygons, 3D cuboids, sensor fusion or behaviour annotation. We return annotated output, an accuracy benchmark against your target, and a programme recommendation. If you scale, a 4-week paid pilot follows — bounded scope, production-grade work, validates fit before scale commitment.
