“tbmaestro uses CVAT as a key tool to structure large-scale annotations for equipment detection on our inspection photos and to accelerate computer vision deployment in asset management.”
CVAT for Infrastructure & Utilities AI
CVAT turns drone, helicopter, and fixed-camera inspection imagery into validated training data for asset inspection, defect detection, and vegetation management, trusted by teams like tbmaestro.
Use cases
Turn Inspection Imagery Into Fewer Missed Defects
Power line and transmission inspection
Label conductors, insulators, cross-arms, and hardware for fault detection.
Vegetation and right-of-way monitoring
Map tree lines and clearance zones to flag encroachment early.
Substation and equipment monitoring
Annotate transformers, breakers, gauges, and thermal hotspots.
Corrosion, crack, and defect detection
Mark rust, spalling, and cracks on towers, bridges, and turbines.
Pipeline and network inspection
Label leaks, joint defects, and blockages from aerial and in-pipe footage.
How it Works
Integrate Seamlessly
Start labeling without changing how you work
Connect your inspection archive, existing labels, and review stages as they are. Nothing to re-plumb.
Book a demoProjects/grid-inspection-q3
Search sourcesAdd source
SourceStatus
1s3://drone-flights/line-patrolsSyncing…
2gs://helicopter-surveys/2026Syncing…
3azure://substation-cctv/archiveSyncing…
4labels_v3 · COCO 1.0Syncing…
5Annotation → Validation → AcceptanceSyncing…
6webhook update:job → trainingSyncing…
7inspection_guidelines_v4.pdfSyncing…
Accelerate Validation
Clear labeling backlogs faster
AI pre-labels every inspection photo. Your team or our experts fix what it misses.
Book a demoJobs/Pole 1187
insulator #1
insulator #2
insulator #3
transformer #4
bird_nest #5
Ship with Confidence
Catch label errors before they reach training
Every job is scored against ground truth, so only clean data reaches training.
Book a demoTasks/Quality control
Mean annotation quality
98.4%
Target 95%
Honeypots
CVAT is how ML teams ship infrastructure AI.
