ENERGY & INFRASTRUCTURE
Power Line & Grid Inspection
Power Line & Grid Inspection. What it takes to run it.
Automated defect detection for insulators, conductors, and towers.
// 01 · The question
“Which towers need a crew this week, and which can wait for the next cycle?”
// 02 · Why it is hard
The defect is often a few dozen pixels against open sky, at a distance the pilot chose for coverage rather than for the model, and the same tower on a different approach is a different image.
// 03 · What the labels have to be
Polygon
Decided before the first frame
A box around a cracked insulator tells you an insulator is there. The dispatch decision needs the extent of the crack, because a hairline and a shattered shed get different trucks. Extent needs an outline.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Pixel-Perfect Data
Annotate power line components, insulators, vegetation encroachment, corrosion, and structural defects with pixel-level precision. Expert-verified for safety-critical accuracy.
LexInsight
Asset Intelligence
Transform isolated defect labels into a queryable asset intelligence graph. Link visual anomalies to asset IDs, maintenance schedules, and SCADA data. Track defect progression over time - not just point-in-time snapshots.
LexAlert
Drift Alerts
Monitor deployed models against seasonal drift, weather changes, and equipment aging. Auto-retrain when accuracy drops, so detection holds as field conditions change.
// 05 · What breaks it after launch
Flight paths get re-planned for airspace, weather or battery range. The asset has not changed; the angle and scale it arrives at have, and the model was fitted to the old ones.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In energy & utilities
Grid Safety & Defect Identification
The Challenge
Severe risk of model degradation caused by rapidly shifting weather patterns, dynamic equipment placement, and evolving site layouts across remote grids.
Our Execution
Engineered safety-critical visual datasets and architected a continuous data ingestion pipeline to instantly process new field imagery as configurations evolved.
The Result
Achieved 21,000+ hazard detections per month, drastically improving frontline worker safety and unlocking frictionless scalability across remote sites.
21,000+
Hazard detections per month