Pavement Crack Mosaicking Based on Crack Detection Quality
Why this work is in the frame
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Bibliographic record
Abstract
Pavement Crack Mosaicking Based on Crack Detection Quality Yeo-San Yoon, Seongdeok Bang, Francis Baek and Hyoungkwan Kim Pages 1197-1201 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: A vehicle-mounted video camera, which is one of low-cost off-the-shelf devices, can be used economically for pavement crack monitoring. The pavement frames obtained by the video camera can be merged to form a mosaic image, from which road distress information can be extracted. However, quality of crack detection in the frames is different from one another. The different level of crack detection quality should be considered for accurate construction of crack mosaic. This paper proposes a new pavement crack mosaicking method based on quality of crack detection in each frame. A convolutional neural network is suggested as a way to evaluate the quality of crack detection in the video frames. The proposed method showed a promising mosaicking performance compared to other existing methods. Keywords: Convolutional Neural Network; Crack Detection Quality; Pavement Crack Mosaicking; Vehicle-mounted Camera DOI: https://doi.org/10.22260/ISARC2019/0160 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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