Evaluating computer vision approaches for counting exposed aggregate number on pavement surface
Bibliographic record
Abstract
Two mainstream solutions for counting the expose aggregate number (EAN) on expose aggregate concrete pavement (EACP) surface are evaluated in this paper. The EAN represents the average wavelength of pavement texture attributed to its correlation. This parameter affects the tire-pavement noise. The EAN is estimated manually by human counting that requires a considerable amount of effort and is time consuming. Recently, computer-vision technologies have accomplished notable success in the counting task. Several state-of-the-art technologies for object counting are proposed for achieving different targets. Therefore, the capability of current states-of-the-art technologies are evaluated to identify if they can be performed for EAN counting tasks because of the complexity characteristic of aggregates. Two deep learning models used for evaluating the EAN counting are Faster-RCNN and LC-FCN. The EACP surface image dataset is constructed for the implemented models. The Tensorflow-Library and Pytorch-Framework are used to fine-tune parameters in the Faster-RCNN and LC-FCN model, respectively. The result indicates that both models achieve a similar accuracy of approximately 70%. The LC-FCN achieves a lower mean absolute error. Further, both methods are preliminarily acceptable for counting the aggregate with their limitation and under a given condition which aggregate is not often occluded and distinguishable between the background and object.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".