1. How is misidentification reduced?
AI detection is trained on over 200,000 crack samples. Apparent features easily confused with cracks, such as water stains and dirt, are a focus of training and tuning; the system reduces misidentification through differences in texture, shape and grayscale. In addition, defect rating results are reviewed by professional engineers to further ensure accurate determination.
2. How accurate is crack measurement?
For width measurement, the 100MP telephoto camera clearly captures 0.1mm-level cracks from a safe distance and measures their width; combined with multi-period mapping, dynamic changes in crack length and width can be observed. Specific misidentification rates and measurement accuracy are affected by imaging quality, lighting and defect characteristics, and are subject to actual operational results.
3. Applicable scenarios for interference recognition
| Scenario | Handling |
|---|---|
| Water stain, reflection interference | Feature-difference recognition + manual review |
| Crack width measurement | 0.1mm-level accuracy |
| Crack development trend | Multi-period comparison observation |
4. Applicable conditions and scenarios
- Areas with heavy interference such as water stains and reflections require focused manual review
- Crack width/length measurement is used for multi-period trend comparison
- Accuracy presupposes imaging quality and site conditions
5. Notes
Any AI detection requires manual review as a safety net; defect determination in complex environments should be confirmed by professional engineers.