Road Defect Detection in Street View Images using Texture Descriptors and Contour Maps
Bibliographic record
Abstract
Road quality assessment is a crucial part in municipalities’ workto maintain their infrastructure, plan upgrades, and manage theirbudgets. Properly maintaining this infrastructure relies heavily onconsistently monitoring its condition and deterioration over time.This can be a challenge, especially in larger towns and cities wherethere is a lot of city property to keep an eye on. We review roadquality assessment methods currently employed, and then describeour novel algorithm aimed at identifying distressed road regionsfrom street view images and pinpointing cracks within them. Wepredict distressed regions by computing Fisher vectors on localSIFT descriptors and classifying them with an SVM trained to distinguishbetween road qualities. We follow this step with a comparisonto a weighed contour map within these distressed regionsto identify exact crack and defect locations, and use the contourweights to predict the crack severity. Promising results are obtainedon our manually annotated dataset, which indicate the viability ofusing this cost-effective system to perform road quality assessmentat a municipal level.
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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 | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".