Evaluating Traffic Signs Detection using Faster R-CNN for Autonomous driving
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
Traffic signs, which provide visual representation, play key role in autonomous navigation. Thus, detection and classification of traffic signs are one of the key requirements in autonomous vehicles (AVs). AVs heavily rely on object detection techniques to classify the traffic signs. In recent years, deep convolutional neural networks (CNNs) such as Faster R-CNN have achieved incredible success on object detection such as traffic signs. This paper focuses on the evaluation of state-of-the-art traffic signs detection techniques using deep learning algorithms and determination of the optimal one that can efficiently detect the traffic signs in real-time. Applying Faster R-CNN, the real-time traffic sign detection shall allow the autonomous vehicles to make decisions in real-time.
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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