Vision-based Vehicle Detection and Distance Estimation
Why this work is in the frame
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Bibliographic record
Abstract
Real-time vehicle detection is one of the most important topics under the Autonomous Vehicles (AVs) research paradigm and traffic surveillance. Detecting vehicles and estimating their distances are essential to ensure that the vehicles can keep a safe distance and run safely on the roads. The technology can also be utilized to determine traffic flow and estimate vehicle speed. In this paper, we apply two different deep learning models and compare their performances in detecting vehicles such as cars and trucks for deployment on the self-driving cars to ensure road safety. Our models are based on YOLOv4 and Faster R-CNN which are efficient and accurate in object detection within a given distance. We also propose a vision-based distance estimation algorithm to estimate other vehicles' distances. In detecting vehicles within 100 meters, the two variations of our models, YOLOv4 and Faster R-CNN, achieved 99.16% and 95.47% mean precision, and 79.36% and 85.54% Fl-measure respectively on a two-way road. The detection speed is 68 fps and 14 fps for YOLOv4 and Faster R-CNN respectively.
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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