GDA-RoadSeg: an improved road segmentation network with gated depthwise attention feature fusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it