RTCNet: A novel real-time triple branch network for pavement crack semantic segmentation
Bibliographic record
Abstract
• A novel real-time triple-branch network was proposed for pavement crack detection under complex scenarios. • A transformer module for enlarging the global receptive field while preserving local details of cracks. • A boundary refinement module was designed to refine crack boundaries. • A manually annotated highway pavement crack dataset with complex scenarios was built for pavement crack detection. Although real-time semantic segmentation of pavement cracks is crucial for road evaluation and maintenance decision-making, it is a challenging task due to low operational efficiency and over-segmentation of existing methods. To address these challenges, in this paper, incorporating Transformers and CNNs, we propose a real-time triple-branch crack semantic segmentation network (RTCNet) using digital camera images. The three branches include a detail branch for capturing local detail features, a context branch for extracting global contextual information, and a boundary branch for obtaining crack boundary information. First, to further enhance crack features, we design a Detail Enhance Transformer (DET) module for enlarging global receptive fields and a Multiscale Aggregation (MSA) module for multiscale learning in the context branch. Second, a Boundary Refinement (BR) module with Sobel operators embedded in the boundary branch is designed to refine the crack boundaries. Last, a Detail-Context Fusion (DCF) module is designed to aggregate the intermediate features extracted from the different branches efficiently Comprehensive quantitative and visual comparisons on four datasets showed that the proposed RTCNet outperforms the comparative models in terms of efficiency and effectiveness with the highest F 1 -score, mIoU, and Frames Per Second (FPS) of 90.56%, 90.25%, and 87.34 in DeepCrack537 dataset, respectively. We also contribute an extensive dataset of pavement cracks, consisting of 464 manually annotated digital images, which is publicly accessible at https://github.com/NJSkate/BeijingHighway-dataset .
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".