Cost-effective LiDAR for pothole detection and quantification using a low-point-density approach
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
Pothole-induced vehicle damage and accidents have significantly increased recently, motivating urgent needs for effective detection and maintenance strategies. This paper introduces an algorithm optimized for low-cost LiDAR sensors that improves the detection and quantification of potholes on road surfaces. The algorithm uses curvature-based analysis to detect potholes in spatially thinned, structured LiDAR datasets and assesses their size through boundary delineation and voxelization. Testing on high-resolution LiDAR scans in Edmonton, Alberta demonstrated consistent detection of varying pothole sizes and shapes, with measurements matching manual LiDAR analysis. Statistical sensitivity analysis revealed that reducing point density significantly to 205 points/m 2 (ppsm) had no measurable impact on detection and geometric assessment accuracy, maintaining measurement errors consistently within 3%–10%. The algorithm proved highly efficient with processing times of 88”/km and 23”/km for test segments with reduced point density, suggesting potential integration with city fleet vehicles for continuous and automated road maintenance monitoring. • Investigating the use of low-cost LiDAR sensors to detect pavement potholes. • Effective algorithms to detect potholes and gauge severity using sparse point clouds. • Investigating the trade-off between pothole characterization and LiDAR point density.
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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