Winter Road Surface Condition Recognition Using Semantic Segmentation and the Generative Adversarial Network: A Case Study of Iowa, U.S.A.
Bibliographic record
Abstract
Adverse road surface conditions (RSCs) are a significant concern for traffic safety and mobility during the winter season. Dealing with this issue requires the prompt execution of maintenance operations to restore safe driving conditions. The faster maintenance personnel are made aware of the dangerous conditions, the faster they can mobilize to mitigate adverse RSCs. Therefore, the speed at which RSC information is provided is vital for reducing potential road-related incidents and maintaining mobility. Intending to improve the delivery of RSC information, this study proposes developing a deep learning-based method through the usage of road weather information system (RWIS) images to automate the RSC classification process. While the RWIS collects images that give a direct view of the road, the traditional manual way of utilizing these images for RSC monitoring is laborious and time-consuming. To overcome this challenge as well as other limitations associated with previously developed RSC recognition methods, the application of deep neural networks using imagery data is investigated here. More specifically, the application of semantic segmentation (SS) and generative adversarial network (GAN) techniques in automating RSC recognition was adopted in an attempt to predict the locations of drivable areas and indicate the snow hazard level solely based on RWIS images. Case study results from Iowa, U.S.A., show that both SS and GAN techniques perform their tasks with a high degree of accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".