Semantic Segmentation and 3D Reconstruction of Concrete Cracks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Damage assessment of concrete structures is necessary to prevent disasters and ensure the safety of infrastructure such as buildings, sidewalks, dams, and bridges. Cracks are among the most prominent damage types in such structures. In this paper, a solution is proposed for identifying and modeling cracks in concrete structures using a stereo camera. First, crack pixels are identified using deep learning-based semantic segmentation networks trained on a custom dataset. Various techniques for improving the accuracy of these networks are implemented and evaluated. Second, modifications are applied to the stereo camera’s calibration model to ensure accurate estimation of the systematic errors and the orientations of the cameras. Finally, two 3D reconstruction methods are proposed, one of which is based on detecting the dominant structural plane surrounding the crack, while the second method focuses on stereo inference. The experiments performed on close-range images of complex and challenging scenes show that structural cracks can be identified with a precision of 96% and recall of 85%. In addition, an accurate 3D replica of cracks can be produced with an accuracy higher than 1 mm, from which the cracks’ size and other geometric features can be deduced.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it