VISION-BASED APPROACHES FOR QUANTIFYING CRACKS IN CONCRETE STRUCTURES
Bibliographic record
Abstract
Abstract. In this paper, a combination of photogrammetric, computer-vision, and deep-learning approaches are proposed for accurate detection and quantification of cracks from the images of concrete structures. In particular, a semantic segmentation approach using UNet is applied, which is trained on a customized dataset of real-world images. Then, two photogrammetric methods are assessed for reconstructing the full figure of the cracks from stereo images. One approach is based on detecting the dominant structural plane surrounding the crack and projecting the crack pixels to this 3D plane. The second approach is based on matching the crack pixels across two images. To be able to perform the 3D reconstructions accurately, a rigorous calibration of the intrinsic calibration parameters of the cameras is performed. The relative orientation parameters between the stereo cameras are also determined in the calibration procedure. Extensive experiments are performed to evaluate each phase of this detection-and-quantification workflow. In general, cracks can be detected with an average precision of 87.48% and recall of 87.45%. They can be reconstructed in 3D with an accuracy as high as 0.05 mm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".