Research on the Method of Detecting the Spreading Rate of the Simultaneous Crushed Stone Sealing Layer Based on Machine Vision
Why this work is in the frame
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Bibliographic record
Abstract
Synchronous chip seal is an advanced road constructing technology, and the gravel coverage rate is an important indicator of the construction quality. The traditional method to measure the gravel coverage rate usually depends on observation by human eyes, which is rough and inefficient. In this paper, a detection method of gravel coverage based on improved wavelet algorithm is proposed. By decomposing the image with two‐dimensional discrete wavelet, the high‐frequency and low‐frequency coefficients are extracted. The noise of the high‐frequency coefficients in the image is removed by improving the threshold function, and the contrast of the gravel target in the low‐frequency coefficients is improved by the multiscale Retinex algorithm, and then two‐dimensional wavelet reconstruction is carried out. Finally, the gravel target is segmented by the block threshold method, and the pixel ratio of the gravel is calculated to complete the detection of the gravel coverage. The experimental results show that the proposed method can effectively segment the gravel target and reduce the influence of environmental factors on the detection accuracy. The detection accuracy error is within ±2%, which can meet the detection requirements. The improved wavelet algorithm improves the signal‐to‐noise ratio of the denoised image, reduces the mean square error, and achieves a relatively good denoising effect.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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