Vehicle Type and Speed Detection on Android Devices Using YOLO V5 and MobileNet
Bibliographic record
Abstract
Vehicle-type detection tool has many applications in transportation, traffic control, guiding and controlling unmanned vehicles, tolls and road taxes, traffic violations, smuggling detection, etc.In the proposed version, the MobileNet neural network and the YOLO V5 algorithm are integrated.In this integration, the YOLO V5 algorithm replaces the convolutional layers of the neural network and the neural network be used for the classification of vehicles.The Kivy library is employed to transform the developed algorithm into an Android application.The data used in this study consists of two datasets: The ImageNet database and a constructed database.The proposed method results show improvement in increasing the accuracy of vehicle detection, reducing the computational load, detection accuracy in different weather conditions, separating overlapping cars.Various methods are presented for better neural network training and reducing neural network size.The reason for these capabilities is the use of developed algorithms and the use of techniques such as data augmentation, spatial filtering, and distillation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".