Improving Concrete Crack Segmentation Networks through CutMix Data Synthesis and Temporal Data Fusion
Bibliographic record
Abstract
A key element in an automated visual inspection system for concrete structures is identifying the geometric properties of surface defects such as cracks. Fully convolutional neural networks (FCNs) have been demonstrated to be powerful tools for crack segmentation in inspection images. However, the performance of FCNs depends on the size of the dataset that they are trained with. In the absence of large datasets of labeled images for concrete crack segmentation, these networks may lose their excellent prediction accuracy when tested on a new target dataset with different image conditions. In this study, firstly, a Transfer Learning approach is developed to enable the networks better distinguish cracks from background pixels. A synthetic dataset is generated and utilized to fine-tune a U-Net that is pre-trained with a public dataset. In the proposed data synthesis approach, which is based on CutMix data augmentation, the crack images from the public dataset are combined with the background images of a potential target dataset. Secondly, since cracks propagate over time, for sequential images of concrete surfaces, a novel temporal data fusion technique is proposed. In this technique, the network's predictions from multiple time steps are aggregated to improve the recall of predictions. It is shown that application of the proposed improvements has increased the F1-score and mIoU by 28.4% and 22.2%, respectively, which is a significant enhancement in performance of the segmentation network.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".