“CVAT helped me build an AI-powered inventory counting solution for warehouse operations.”
CVAT for Transportation & Logistics AI
CVAT turns warehouse, yard, and fleet camera footage into validated training data for inventory counts, freight handling, and driver safety, trusted by teams like Deloitte.
Use cases
Turn Warehouse and Yard Footage Into Accurate Counts
Inventory and pallet tracking
Count pallets, cartons, and stock locations for every cycle count.
Parcel sorting and damage detection
Label parcels, shipping labels, and damage on sortation lines.
Yard, dock, and terminal operations
Track trucks, trailers, and dock doors to measure dwell time.
Fleet safety and driver behavior
Flag tailgating, lane drift, and near misses in dashcam footage.
Rail and port asset monitoring
Track containers, railcars, and cranes across terminals and yards.
How it Works
Integrate Seamlessly
Start labeling without changing how you work
Connect footage from every site, existing labels, and review stages as they are. Nothing to re-plumb.
Book a demoProjects/dc-network-q3
Search sourcesAdd source
SourceStatus
1s3://warehouse-cctv/dc-04Syncing…
2gs://fleet-dashcams/2026Syncing…
3azure://yard-cams/terminal-2Syncing…
4labels_v2 · COCO 1.0Syncing…
5Annotation → Validation → AcceptanceSyncing…
6webhook update:job → trainingSyncing…
7labeling_guidelines_v3.pdfSyncing…
Accelerate Validation
Clear labeling backlogs faster
AI pre-labels every frame. Your team or our experts fix what it misses.
Book a demoJobs/Dock 7 · Frame 2318
pallet #1
pallet #2
pallet #3
forklift #4
pallet #5
Ship with Confidence
Catch label errors before they reach training
Every job is scored against ground truth, so miscounts never reach training.
Book a demoTasks/Quality control
Mean annotation quality
98.4%
Target 95%
Honeypots
CVAT is how ML teams ship logistics AI.
