GDA-RoadSeg: an improved road segmentation network with gated depthwise attention feature fusion
Bibliographic record
Abstract
Road segmentation is an important and challenging task for robotics working in unstructured environments. There are some problems that need to be solved, such as low efficiency in multi-scale feature fusion, insufficient long-range dependency modeling, and inaccuracy of edge segmentation. To address these issues, an efficient road segmentation model based on ResNet34 is proposed in this paper. First, we design a gated depthwise attention fusion module (GDAFM), which dynamically fuses shallow-detail and deep-semantic features via a spatial attention mechanism and depthwise convolution to improve fusion efficiency. Second, we proposed an enhanced asymmetric dilated block (EADB) by employing a large horizontal dilation rate to strengthen long-range dependency modeling and optimizing parameters to eliminate the gridding effect. Additionally, we introduce an edge-aware auxiliary branch (EAB), combining automatically generated edge supervision signals with a multi-task loss function to significantly boost boundary accuracy. Experiments on the Cityscapes and CamVid datasets show that our model achieves MaxF scores of 97.89% and 97.46%, respectively. The results show that our model outperforms other state-of-the-art models.
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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 | 0.001 |
| 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) | 0.003 | 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".