Evaluation of Asphalt Pavement Crack Sealing Performance Using Image Processing Technique
Bibliographic record
Abstract
Evaluation of Asphalt Pavement Crack Sealing Performance Using Image Processing Technique Hyoungkwan Kim, Hamid Soleymani, Seung Heon Han, Hana Nam Pages 341-345 (2006 Proceedings of the 23rd ISARC, Tokyo, Japan, ISBN 9784990271718, ISSN 2413-5844) Abstract: Crack sealing is a routine and necessary operation of pavement maintenance. Manual observation of road surfaces has been the most common method for evaluating road surface cracks around the world. However it is difficult to objectively and accurately assess the road cracks based on human visual perception. The ultimate objective of this study is to evaluate crack sealing performance on highways, in order to choose the best crack sealing practice in an automated manner. As a preliminary step, this paper discusses how to define crack sealing performance and propose a research methodology to quantify the level of road surface distress using video image processing. Keywords: crack sealing, image processing, pavement DOI: https://doi.org/10.22260/ISARC2006/0066 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".