AI-Powered Drone Defect Detection & Asset Mapping
Hover Aerial Imaging captures the high-fidelity visual data your AI defect detection workflows need. Operating under CASA ReOC #9398, we fly purpose-built grid patterns over concrete, steel, and composite structures, capturing imagery dense enough to resolve features down to 0.2 millimetres.
Every defect identified is geolocated onto the 3D mesh, producing a data-driven map for prioritised remediation and longitudinal asset tracking. We then produce a PDF defect report outlining every identified feature, approximate size, location, and severity.
How does drone-based AI defect detection actually work?
The AI defect detection pipeline has three stages. First, we capture ultra-high-resolution oblique imagery flown in tight standoff patterns, with overlap densities engineered for 3D reconstruction.
Second, the imagery is photogrammetrically processed into a textured 3D mesh and orthomosaic. Every pixel of the asset's surface is georeferenced.
Third, the dataset is ingested into a machine learning algorithm trained to flag micro-fractures, concrete spalling, corrosion, delamination, vegetation encroachment, or other anomalies you specify.
The algorithm outputs a georeferenced defect register: each defect tied to a coordinate, a defect class, and a confidence score.
What can the data resolve?
Our visual data resolves features down to 0.2 millimetres. That means hairline concrete cracks, earlystage spalling, micro-corrosion on steel members, paint and coating breakdown, and vegetation encroachment on insulator strings.
The resolution depends on three things: optical payload selection, flight standoff distance, and lighting conditions.
We tune each project's flight plan to the smallest defect class your asset condition standards require.
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AI-ingestion-ready 3D mesh (.OBJ, .FBX) — sub-millimetre textured model, formatted for direct ingestion into the machine learning pipeline
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High-resolution georeferenced orthomosaic (.GeoTIFF) — millimetre-grade asset surface map
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Defect register (.CSV, .PDF) — every flagged anomaly with coordinates, classification, photo crop, and confidence score
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Spatial defect map — interactive 3D model with defect markers overlaid for engineer review
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Capture metadata — sensor specifications, GSD, GCP residuals, and CASA flight authorisation references
What does the deliverable include?
Concrete spillways, bridge decks and undersides, dam walls, cooling towers, processing plants, road bridges, rail bridges, large-scale tile and panel facades, silos, chimneys, and high-voltage transmission insulators.
Anywhere manual inspection requires rope access, scaffolding, EWPs, or confined-space entry, and anywhere change-detection over time is the actual procurement need. We replace the access risk, not the inspecting engineer.
Which assets benefit most from drone AI defect detection?
Frequently Asked Questions
How small a defect can your drone data actually resolve?
Down to 0.2 millimetres on appropriate surfaces, using ultra-high-resolution optical payloads with tight standoff distances. For larger or more complex assets where 0.2mm is unnecessary, we tune the GSD to the relevant defect class. Typically 1mm for general concrete inspection, 0.5mm for hairline crack detection, 0.2mm for early-stage spalling identification.
Do you run the AI yourself, or just supply the input data?
We run proprietary internal AI algorithms and partner with external providers depending on the class detection needs, project scope, and deliverables required
Can defects be tracked over time?
Yes. We log flight plans, GCP coordinates, and capture parameters for every project so a follow-up capture in 6, 12, or 24 months is registered to the same coordinate frame. Side-by-side comparison and automated change detection becomes straightforward.
Is this approach engineer-defensible? Will an asset owner accept it?
The drone capture replaces the visual access method. The engineer still reviews and signs off on the asset condition. Defect registers, photographs, geolocation, and confidence scores are all appended to a written engineer's report. Major asset owners across rail, transport, and utilities have accepted droneled inspection workflows for over a decade.
What does AI defect detection cost compared to manual inspection?
Drone-based AI inspection typically reduces inspection cost by 30–60% on assets requiring rope access, EWPs, or scaffolding — driven primarily by access cost elimination and parallel-withconstruction operability. A typical concrete spillway capture is $2,000–$6,000; a multi-span bridge $3,500–$15,000. Manual rope access on the same assets routinely costs 2–5x this.