Evaluation of Alternative Pre-trained Convolutional Neural Networks for Winter Road Surface Condition Monitoring
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
Real-time winter road surface condition (RSC) monitoring is of critical importance for both winter road maintenance operators and the travelling public. Accurate and timely RSC information during snow events can help maintenance operators to deliver better maintenance services, such as plowing and salting, for reduced costs and salt usage and improved level of service. With this information, the traveling public can make more informed decisions on whether or not to travel, where to go, and which highways to drive on. In our previous effort we have shown the potential of applying a pre-trained convolutional neural network (CNN) for automatically detecting winter road surface conditions based on images from fixed traffic/weather cameras or in-vehicle devices. This paper focuses on comparing the performance of four most successful CNN models available from the leaders of this technology, namely, VGG16 (Oxford University), ResNet50 (Microsoft), Inception-V3 (Google) and Xception (Google), for solving the RSC classification problem. The models were first customized with additional fully-connected layers of neurons for learning the specific features of the RSC images. The extended models were then trained with a low learning rate for fine-tuning by using a small set of RSC images. The models were tested using a hold-out set of images from cameras installed at different locations, showing highly encouraging results.
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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 it