A Regional-Level Resource-Saving Model for Winter Road Surface Snow Detection in Extreme Weathers
Bibliographic record
Abstract
Achieving timely and accurate snow detection on road surfaces in extreme weather conditions is vital for both transportation and computer vision applications. However, conventional object detection models, particularly those designed for small targets, fall short in addressing the challenge that is posed by special regional-level multiscale recognition task. To this end, an end-to-end precise and swift road surface snow detection architecture, termed the Resource-Saving Snow Detect Model (RSSD) that includes a multidimensional directional attention mechanism, is proposed. In this model, we designed three dedicated modules, namely Multi-dimensional Bidirectional Attention Module (MDBA), Split-EMA-Convolution (SEC) and Equal Split Convolution (ESC), to address the essential feature extraction and fusion tasks in snow detection. MDBA is able to promote lateral interaction and comprehensive feature fusion across scales, while SEC can not only enhance feature extraction for regional awareness but also reduces computational load, making it efficient under minimal computational power consumption. ESC preserves feature height fusion while significantly reducing computational costs, thereby enhancing the real-time detection capability of the model. In experimental evaluations conducted with data collected by in-vehicle cameras from various roads in the United States and Canada, the results demonstrate higher detection accuracy and speed compared to the latest Transformer-based real-time object detection methods and other exiting methods in the literature. Furthermore, we validated the model's performance and data sensitivity through semi-supervised learning with 50,000 unlabeled images. This research holds significant implications for winter road traffic and provides valuable insights for similar computer vision tasks.
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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 | 0.000 |
| 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".