Deep faster R-CNN-based automated detection and localization of multiple types of damage
Bibliographic record
Abstract
The primary method of structural health monitoring is human-based visual inspection, which—despite its limitations of consistency and accessibility—can warn about changes in a bridge’s condition. To improve the visual inspection of civil infrastructure and address these drawbacks of human-oriented inspection, computer vision-based techniques have been developed to detect structural damage in images. Most of these methods, however, detect only specific types of damage, such as cracks in concrete or steel. Another drawback is that the traditional convolutional neural network-based damage detection method is not able to provide the location of the detected damage. To provide quasi-realtime simultaneous detection and localization of multiple types of damage, a structural damage detection method based on Faster Regionbased Convolutional Neural Network (Faster R-CNN) is proposed. The original architecture of Faster R-CNN is modified, trained, validated, and tested for this study. The robustness of the trained Faster R-CNN is evaluated and demonstrated using seven new images taken of various structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".