A manmachine balanced rapid object model for automation of pavement crack sealing and maintenance
Bibliographic record
Abstract
A number of studies during the last few years have discussed automated crack detection and mapping using digital image processing technologies in roadway maintenance and rehabilitation. Many recent studies have applied digital image processing to the recognition or sealing of cracks in pavement. There have been great discrepancies, however, among various segmentation methods that extract crack types and locations or classify the extent of cracking. Since all sensing systems also produce some spurious data and experience noise due to the varied topological and color conditions of the pavement surface, accurately mapping and representing the pavement cracks to be sealed using such segmentation methods would be even harder. This paper illustrates an innovative machine vision algorithm developed for accurate crack mapping and representation in the University of Texas (UT) automated road maintenance machine (ARMM). The paper mainly focuses on illustrating the detailed logic and descriptions of the algorithm. Efficiency evaluation results of the ARMM manmachine balanced crack mapping and representation process, including the line-snapping and path-planning functions, are also shown. Using the algorithms as an edge-describing tool can have broader applications in automation of infrastructure maintenance and inspection of civil works and in the domain of digital image processing.Key words: automation, image processing, line snapping, pavement, crack sealing, maintenance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".