Application · Infrastructure Intelligence
Automated defect detection across inspection imagery
Infrastructure Intelligence reviews inspection images at volume, classifies and locates structural defects, quantifies affected surface area, and keeps a per-asset condition record that carries forward between inspections.
The problem
Manual review does not scale with asset portfolios
Inspection imagery arrives faster than engineers can review it. Assessment quality depends on who reviewed it and when, findings sit in documents rather than a queryable record, and comparing this year's condition to last year's is a manual exercise.
Who it is for
- Asset owners and maintenance authorities managing bridges, buildings, roads, tunnels and utilities
- Industrial and energy operators inspecting plants and structures
- Inspection service providers running drone and field programmes
- Engineering teams responsible for structural condition assessment
Mechanism
How it works
Upload inspection imagery from any source, the models detect and measure defects, and the output lands in reports and downstream systems.
1 — Ingest
Images from drones, mobile devices or integrated third-party systems are uploaded or pushed via API. Existing archives can be processed alongside new captures.
2 — Detect and quantify
Computer vision models classify defects, adjust bounding boxes to the defect extent, and estimate affected surface area to support cost and scope estimation.
3 — Report and track
Findings are compiled into reports aligned to your inspection format, mapped onto 3D models, and retained as history for trend analysis.
AI models
Defect types currently detected
- Crack detection
- Rebar exposure detection
- Spalling detection
- Efflorescence detection
- Rust and corrosion detection
- Honeycombing detection
Detection coverage is extended as data pipelines expand. Model performance on your asset class should be validated during a pilot on your own imagery rather than assumed from a published figure.
Output
What the team receives
- Classified defect records with location and bounding geometry
- Surface area quantification to support cost estimation
- Automated inspection reports aligned to reporting standards
- Asset condition history across inspection cycles
- Predictive maintenance signals derived from condition trends
- 3D defect mapping viewable in the MRLAYOUT spatial layer
- API integration into ERP, cloud platforms and drone management systems
Questions buyers ask
Practical detail
What data does it need to start?
Does it replace the inspecting engineer?
How does it fit existing workflows?
How does it connect to the rest of ViSNET?
Where it sits
Inside the platform, not beside it
Infrastructure Intelligence is the applications layer of the same chain every ViSNET deployment uses.
- 01
Physical world
Bridges, plants, sites, buildings, workforce and work in progress.
- 02
Visual data capture
Existing CCTV, drones, mobile devices, sensors and VR/MR spatial capture.
- 03
ViSNET AI intelligence layer
Computer vision, spatial reconstruction and analytics that turn imagery into structured records.
- 04
Applications
Infrastructure Intelligence · MRLAYOUT · EHS & Workforce Intelligence.
- 05
Workflows and decisions
Inspection reports, attendance records, digital twins, forecasts and alerts inside existing operations.