InFlightAI Computer Vision Model
Equipment Tag Reading
AI Model Overview
InFlightAI’s Equipment Tag/Label Reading model uses AI-powered OCR and image processing to automatically detect and extract asset identifiers from labels, tags, and nameplates. It supports varied formats, orientations, and lighting conditions, enabling accurate asset tracking, inventory management, and inspection workflows.
OCR is tuned for industrial labels — stamped, printed and engraved — rather than document text, and handles the off-angle, weathered and low-contrast tags that a handheld scanner rejects. You define which regions of the image are read, so the model works on the nameplate rather than the whole scene, and filters to the line you need when a label carries several.
Nameplates and tags are read in the field, so inspection records attach to the right asset rather than the right approximate location. The asset register stays current as a by-product of inspection instead of becoming a separate project.
How It Works
Equipment Tag/Label Reading returns digitized text output. It reads asset identifiers from labels, tags and nameplates, including alphanumeric text with commas, periods and special characters, across varied label formats and orientations. Up to four labels can be read from a single image, with targeted line selection where multiple lines are present.
Equipment Tag/Label Reading is an asset maintenance model, currently deployed across industries like Electric Utilities, Oil & Gas, Manufacturing, Logistics, Power Generation, Nuclear and Mining. 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.