Inspect, detect, and digitise every grid asset.
Combine LiDAR digital twins with multi-modal AI inspection — visual, thermal, and corona/UV analysis — to build a living asset registry that guides maintenance priorities and field decisions for transmission and distribution operators.
Grid Asset Inspection
NeuroCluster runtime
Queue
Capability match
Visual + Thermal + UV
Inspection types
2-stage YOLO
Detection pipeline
PostGIS geo-spatial
Asset registry
Required
HITL
The challenge
TSOs and utilities inspect hundreds of kilometres of lines and thousands of towers using manual field teams, aerial surveys, and disconnected reports. Defect data sits in PDFs and spreadsheets; asset condition is never current. Thermal hotspots and corona discharge go undetected until failure.
How it works
A unified inspection platform ingests drone imagery, LiDAR point clouds, thermal, and UV camera feeds. A two-stage YOLO pipeline detects components and classifies defects with confidence scores. Human inspectors verify or reject AI detections. A PostGIS asset registry tracks condition over time; professional PDF reports are generated automatically.
Outcomes
- Full digital twin of grid infrastructure built from LiDAR and inspection data
- Thermal hotspots and corona discharge detected before failure
- Human verification loop ensures no unreviewed AI finding reaches maintenance dispatch
- PDF inspection reports with risk assessments generated automatically per asset
Capabilities
LiDAR digital twin
3D point-cloud reconstruction of grid infrastructure — towers, conductors, and right-of-way.
Multi-modal AI detection
Two-stage YOLO pipeline: first detect components, then classify defects across visual, thermal, and UV imagery.
Thermal hotspot analysis
Infrared camera processing to identify and grade severity of hot spots before failure.
Corona / UV discharge detection
UV camera analysis to surface insulator corona discharge and early degradation signals.
Human-in-the-loop verification
Inspectors confirm or reject every AI detection — no finding dispatched to maintenance without review.
Automated inspection reports
PDF and CSV reports with risk assessments, defect galleries, and condition summaries per asset.
Workflow
- 1
Ingest
Upload drone images, LiDAR scans, thermal, and UV footage via secure tenant channel.
- 2
Detect
AI pipeline identifies components and classifies defects with confidence scores.
- 3
Verify
Field inspectors review AI detections, confirm findings, and flag false positives.
- 4
Report
System generates PDF inspection reports and updates asset registry condition scores.
Platform stack
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