From capture to clarity in a single workflow

Slide to show example AssetScan overlay
Reds = open cracking; Blues = self-healed cracks; Greens = calcite formations

AssetScan is a proprietary machine vision technology that uses photographic data captured by UAVs, drones and mobile camera systems to analyse and map signs of structural deterioration.

​Traditional asset inspections can be costly, high-risk and inconsistent. AssetScan replaces the manual interpretation of large image datasets with AI-driven analysis. It can also contextualise features of interest within 3D or 2D digital twin models, enabling engineers to visualise and monitor deterioration quickly, consistently and safely.

The system is already well developed in identifying certain types of defects affecting concrete, masonry, steelwork and embankments. It can also be configured to identify new defects and other features of interest through bespoke applications tailored to specific project requirements.

Enhances safety by reducing risks taken during manual inspections (roped access, working at height, confined spaces, etc).

Provides accurate 2D and 3D mapped deterioration data.​

Speeds up maintenance decision-making and supports compliance with regulatory requirements for visual inspections.

Leverages comprehensive image datasets captured during automated and semi-automated robotic surveys, enabling automated change detection across repeat surveys.

Applications
Built for any surface. Detect issues with confidence.

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How It Works
From capture to insight in five streamlined steps

Machine Vision

AssetScan uses trained algorithms to detect and classify structural defects consistently, reducing interpretation costs and improving accuracy. The system also enables automated change detection across repeat surveys, allowing areas of concern to be identified quickly for review by an engineer.

Exportable 2D and 3D models compatible with CAD and BIM software.

Georeferenced meshes and point clouds.

Condition overlays for targeted maintenance planning and change detection.