1. Why is real-time detection not possible?
The drone collects full-bridge HD photo data along routes; a 500m-class bridge generates nearly 100GB of data and over ten thousand photos, far exceeding the drone’s real-time processing capability, so data must be processed after return. A single 100MP photo is 40-50MB, and edge-side compute and storage cannot support real-time AI detection.
2. What is the complete detection workflow?
Data is uploaded to the platform, where AI automatically screens and detects defects, followed by manual review and 3D model mapping, forming a complete inspection workflow: capture → upload → AI detection → manual review → 3D mapping → report output. This model ensures the detection model can be centrally iterated and updated, producing more stable recognition quality.
3. Real-time vs platform-based detection
| Method | Capability | Applicable to |
|---|---|---|
| Edge real-time detection | Limited, data volume unsupported | Lightweight scenarios |
| Platform unified detection | Iterable model, stable accuracy | Standard bridge inspection workflow |
4. Applicable conditions and scenarios
- Workflow separating field collection from office-based detection
- Reliance on platform compute and unified AI model processing
- Bridge inspection projects with large data volumes and high accuracy requirements
5. Notes
Although real-time detection can shorten wait time, it is limited by edge compute and model updates; bridge defect rating should follow platform unified detection + manual review.