Point Cloud Semantic Segmentation of Concrete Surface Defects Using Dynamic Graph CNN
Bibliographic record
Abstract
Obtaining accurate information of defective areas of infrastructures helps to perform repair actions more efficiently. Recently, LiDAR scanners are used for the inspection of surface defects. Moreover, machine learning methods have attracted the attention of researchers for semantic segmentation and classification based on point cloud data. Although much work has been done in the area of computer vision based on images, research on machine learning methods for point cloud semantic segmentation is still in its early stages, and the current available deep learning methods for semantic segmentation of the concrete surface defects are based on converting point clouds to images or voxels. This paper proposes an approach for detecting concrete surface defects (i.e. cracks and spalls) using a Dynamic Graph Convolutional Neural Network (Dynamic Graph CNN) model. The proposed method is applied to a point cloud dataset from four concrete bridges in Montreal. The experimental results show the usefulness and robustness of the proposed method in detecting concrete surface defects from 3D point cloud data. Based on the sensitivity analysis of the model using three cases defined with different number of input points, the best test results show the detection recall for cracks and spalls are 55.20% and 89.77%, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".