Crack-U<sup>2</sup>Net: Multiscale Feature Learning Network for Pavement Crack Detection From Large-Scale MLS Point Clouds
Bibliographic record
Abstract
Deep learning-based algorithms detect pavement cracks in an end-to-end manner from Mobile Laser Scanning (MLS) point clouds, achieving impressive results. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding multiscale features and the limited training data. In this paper, we propose a novel pavement crack detection framework, Crack-U2Net, which innovatively incorporates a two-level nested U-Net architecture for feature learning. This design enables the learning of intra-stage multiscale features without introducing significant memory and computation costs, resulting in substantial improvements in accuracy. Moreover, to solve the challenge of insufficient training data, we propose a Geometry-based Data Augmentation (GDA) strategy, aiming to expand the pavement dataset while preserving the pavement geometry. Extensive experiments on the Qinghai-Tibet Highway point cloud dataset demonstrate the higher accuracy and efficiency of Crack-U2Net over the state-of-the-art methods, achieving an average precision, recall, F$1\text - $score, and accuracy of 83.8%, 77.6%, 80.1%, and 95.8%, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".