Tyre (Tire) Brand and Size Detection with Computer Vision
Bibliographic record
Abstract
It is crucial that end users must choose the appropriate tire according to vehicle’s requirements and standards for safety. With the increase in demand for e-commerce channels due to the change in consumer habits, users who want to buy tires online have great difficulty in distinguishing the necessary brand and size information from the surface of the tire and their customer experiences are interrupted without purchasing. To sort a solution out for this pain point, a model based on image processing and supervised deep learning algorithms has been developed to find required information for purchasing a tire such as brand and size from a single image. Proposed model detects the vehicle tire and makes the input image suitable for reading the information on it. Then the brand and size are determined with the segmentation deep learning models which are trained on the problem specific dataset prepared by the authors. The proposed model is one of the working examples that offers an end-to-end solution for online tire purchasing process of customers, with a success rate of 97.3% in brand detection and 95.1% in size detection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".