InFlightAI Computer Vision Model
Insulator Condition Inspection
AI Model Overview
InFlightAI’s Insulator Condition Inspection model uses computer vision AI to detect damage, flashing, contamination, and degradation in electrical insulators. It identifies cracks, chips, and buildup that may impact performance, supporting preventative maintenance and reducing the risk of outages.
A detection model locates each insulator, then a second classification pass grades its condition. Training draws on both real and synthetic imagery, covering damage patterns that are rare in the field. Reliable detection needs the insulator to fill enough of the frame — roughly four to twelve bells visible at standard zoom — and the model is configured to the insulator types on your network during onboarding.
Cracking, chipping and flashover damage are caught before they become a fault. Severity grading means replacement is scheduled by condition rather than by age.
How It Works
Insulator Condition Inspection returns bounding box output. It identifies cracks and chips, flashover scorching from electrical discharge, and contamination or surface buildup across white and brown porcelain, polymer and mushroom insulators. Damage is graded minor, major or severe.
Insulator Condition Inspection 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 configured to the assets and environments at your site during onboarding, and is ready for use within thirty days.