Accurate Positioning of License Plate in Video Stream Based on Concatenated Convolutional Neural Network
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".