Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images
Bibliographic record
Abstract
Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".