Development of a Highly Transferable Urban Winter Road Surface Classification Model: A Deep Learning Approach
Bibliographic record
Abstract
Road surface condition (RSC) is an important performance indicator for winter road maintenance personnel to maintain safe driving conditions. This becomes more apparent in inclement weather events where timely clearing of snow is highly prioritized. Considering the vast road networks that need to be covered, many transportation agencies have been using camera images to view real-time RSC directly; however, monitoring conditions via these cameras is still being done manually, thereby hindering its full utilization for optimizing maintenance services. Many studies have attempted to develop a deep-learning-based approach known as convolution neural network (CNN) to automate the process of RSC image classification. When implemented, RSC can be extracted from road images without human involvement. However, efforts made thus far have been focused on rural highways, with performance in the urban context being the least explored. Furthermore, CNN models developed in previous studies have been trained either from scratch or via transfer learning, but only a few studies have investigated transfer learning using a pre-trained RSC model. To address these gaps, an urban RSC model was developed in this study via transfer learning using a pre-trained RSC CNN model. The image dataset used contains 3914 urban images collected in a residential area south of Edmonton, Alberta. With these images, the pre-trained RSC model was fine-tuned via transfer learning and underwent hyperparameter optimization to boost performance further, yielding a high classification accuracy of 98.21% and an F1-score of 98.4%, which surpassed the accuracy of the model trained from scratch.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".