1. Why do false positives and missed detections occur?
AI models learn defect features from training samples. When facing unseen differences in structure, lighting or texture, false positives (judging non-defects as defects) or missed detections (missing real defects) can occur. This is an inherent characteristic of all machine vision systems — the probability is extremely low but cannot be reduced to zero.
2. How does the dual mechanism avoid risk?
| Mechanism | Role |
|---|---|
| Massive sample continuous iteration | Improves model detection accuracy |
| Mandatory manual review | Engineers verify and correct all detection results |
RIEJIAN’s training samples now exceed 1.2 million engineering real-scene defect images, with the model continuously iterated; meanwhile, all AI detection results must be reviewed by engineers — dual assurance that detection results are accurate and reliable.
3. How is reliable detection ensured?
Only detection results verified and corrected through manual review can be output as report basis; final defect level rating and authenticity determination are completed by engineers, procedurally preventing false positives and missed detections from entering formal reports.
4. Applicable conditions and scenarios
- Formal inspection projects requiring highly reliable detection results
- Complex-structure bridges where defects are easily confused
- Owner organizations with strict requirements on result accuracy
5. Notes
AI detection results serve only as initial screening and assistance; the formal conclusion is subject to engineer review results.