License plate image patch filtering using HOG descriptor and bio-inspired optimization
Bibliographic record
Abstract
Automatic license plate detection (ALPD) is one of the widespread research topics in the area of intelligent transportation systems. A challenging issue that affects ALPD performance is the complex image background, where possibility of misclassification of non-license plate (non-LP) objects as a license plate (LP) object is high. One of the ways to resolve the issue is to use an efficient filter to correctly classify the license plate and non-license plate objects. In this paper, we propose an efficient general technique for the classification of LP and non-LP images based on Histogram Oriented Gradient (HOG) features, and mixture of experts model (binary classifiers). To maximize the classification performance, Genetic Algorithm (GA) is applied to find the best feature subset and adjust the weights of the mixture model. Performance of the proposed method is evaluated on a new database of 2360 LP and non-LP images created by us. Experimental results achieve image classification with a high accuracy of 85.16%. The filter is also tested by plugging it into a recent ALPD system which improves the detection performance by 6.7%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".