SD-GCN: Saliency-based dilated graph convolution network for pavement crack extraction from 3D point clouds
Bibliographic record
Abstract
Accurate pavement crack extraction is significant for pavement routine maintenance and potential traffic disaster minimization. Due to unordered data formats, intensity distinctions, and crack shape variations from point clouds captured by mobile laser scanning (MLS) systems, many preceding rule-based approaches and learning-based approaches cannot achieve high extraction accuracy and efficiency. To tackle these problems, we develop a saliency-based dilated graph convolution network, named SD-GCN, for pavement crack extraction from MLS point clouds. This network mainly consists of four modules. First, Module I is designed to remove off-ground point clouds. Next, two feature saliency maps are constructed to leverage both height and intensity information in Module II. Then, in Module III, the inherent point features and high-level edge features in multiple local neighborhoods are further extracted using a cylinder-based dilated convolution strategy. Finally, an MLP-based net architecture is designed for crack extraction refinement in Module IV. Experimental results exhibit that the SD-GCN model delivers an average of precision, recall, and F1-score of 79.5%, 77.1%, and 78.3%, respectively, which outperforms state-of-the-art methods in terms of extraction accuracy and computational efficiency.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".