Accurate Positioning of License Plate in Video Stream Based on Concatenated Convolutional Neural Network
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Bibliographic record
Abstract
One of the key functions of intelligent traffic management system is the accurate positioning of license plate in the video stream. However, the traditional license plate positioning algorithms are greatly affected by environmental factors, such as license plate covers, cloudy weather and varied colors. To overcome this defect, this paper designs a three-level concatenated convolutional neural network (CCNN) with multi-task learning ability. The first level detects the vehicles in the video, using the target detection algorithm You Look Only Once, Version 3 (YOLO v3). Based on the images detected on level 1, the second level performs rough detection of the license plate. On this basis, the third level accurately positions the key points on the license plate. The experimental results show that the CCNN achieved a mean accuracy of 95.8 % and a positioning speed of 63f/s in license plate detection, much better than the traditional license plate positioning algorithms. The proposed method can pinpoint the license plates in video in real time at a high accuracy.
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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) | 0.001 | 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