ENERGY & INFRASTRUCTURE
Pipeline & Leak Detection
Pipeline & Leak Detection. What it takes to run it.
Aerial patrol analysis for leaks, encroachment, and corrosion.
// 01 · The question
“Where along this right-of-way is product escaping, and who is digging near it?”
// 02 · Why it is hard
A patrol covers hundreds of miles to find something present in a handful of frames. The class balance is brutal, and a model that predicts nothing at all scores extremely well on the average frame.
// 03 · What the labels have to be
Instance segmentation
Decided before the first frame
A leak has no edge two people would draw the same way. Sheen on soil, a stressed patch of vegetation, a vapour plume: all regions with soft boundaries, and a box around one is mostly ground. The mask is the measurement, because area is what tells you whether it is growing.
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
Wet ground, low sun and haze all change what a sheen looks like from the air, and the conditions that make leaks likeliest are the conditions the model sees least often in training.
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