RdmkNet & Toronto-RDMK: Large-Scale Datasets for Road Marking Classification and Segmentation
Bibliographic record
Abstract
Effective road marking classification and segmentation play a pivotal role in advancing vehicle-to-everything (V2X) applications and refining road inventory databases. However, the irregular data formats and unordered permutation modes of 3D point clouds, along with the limited availability of large-scale datasets with point-level annotations, remain significant obstacles to designing deep learning-based networks with superior performance. To address these challenges, this paper proposes a novel multi-level feature optimization network structure, named MFPNet, and introduces two point cloud benchmarks, RdmkNet and Toronto-Rdmk, for road marking classification and segmentation in intricate urban environments. MFPNet is composed of three integral modules. First, the M-transformer module, consisting of three transformers obtained from different channels, fully captures rich point cloud background information and long-distance dependencies between objects. Then, the feature pooling aggregation module uses parallel structured pooling attention mechanisms to aggregate features captured by the M-transformer module, while the prediction refinement module further enhances the acquisition of semantic features. Comparative studies indicate that MFPNet can be embedded into general deep learning networks without changing their original network structures, significantly improving the accuracy of multiple baseline networks. Furthermore, extensive experiments demonstrate that the two newly-developed point cloud datasets are meaningful for road marking classification and segmentation tasks, contributing to the development of autonomous driving.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".