InFlightAI Computer Vision Model
Utility Pole Status
AI Model Overview
InFlightAI’s Utility Pole Status model uses computer vision AI to assess the structural condition of utility poles. It detects issues such as leaning, cracking, rot, and physical damage, providing actionable insights for maintenance prioritization and infrastructure reliability.
The pole is detected, then assessed for lean against vertical and for surface condition including cracking and decay. Severity is graded so results sort into a prioritized list.
Leaning, cracked and rotting poles are identified before failure takes the circuit down. Severity ranking directs replacement budget to the poles most likely to fail, across the full pole population rather than a sampled subset.
How It Works
Utility Pole Status returns segmentation mask output. It identifies leaning poles, cracking and splitting, rot and material decay, and physical damage.
Utility Pole Status is an asset maintenance model, currently deployed across industries like Electric Utilities and Power Generation. Detections are returned after the flight has landed, once imagery and waypoint data have been synced to the platform. The model is ready from day one, with no site-specific configuration required.