A FEASIBILITY STUDY ON USE OF GENERIC MOBILE LASER SCANNING SYSTEM FOR DETECTING ASPHALT PAVEMENT CRACKS
Bibliographic record
Abstract
This study aims to automatically detect pavement cracks on urban roads by employing the 3D point clouds acquired by a mobile laser scanning (MLS) system. Our method consists of four steps: ground point filtering, high-pass convolution, matched filtering, and noise removal. First, a voxel-based upward growing method is applied to construct Digital Terrain Model (DTM) of the road surface. Then, a high-pass filter convolutes the DTM to detect local elevation changes that may embed cracking information. Next, a two-step matched filter is applied to extract crack features. Lastly, a noise removal process is conducted to refine the results. Instead of using MLS intensity, this study takes advantages of the MLS elevation information to perform automated crack detection from large-volume, mixed-density, unstructured MLS point clouds. Four types of cracks including longitudinal, transvers, random, and alligator cracks are detected. Our results demonstrated that the proposed method works well with the RIEGL VMX-450 point clouds and can detect cracks in moderate-to-severe severity (13 - 25 mm) within a 200 m by 30 m urban road segment located in Kingston, Ontario, at one time. Due to the resolution capability, small cracks with slight severity remain unclear in the MLS point cloud.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".