Détection des panneaux de signalisation routière par accumulation bivariée
Bibliographic record
Abstract
Nous présentons une méthode géométrique utilisant l’orientation du gradient pour la \ndétection de la signalisation verticale dans des images fixes, indépendamment de leur position \net de leur orientation. La détection est réalisée par une transformation de type accumulateur \nde Hough bivariée, fondée sur l’utilisation de paires de points avec des contraintes sur leurs \ngradients. Les panneaux circulaires et polygonaux (non triangulaires) sont détectés par la \ntransformation chinoise bilatérale TCB. Cette transformation est rapide et ne fait pas de distinction \nentre les cercles et les polygones 4 côtés ou plus. Le cas des panneaux triangulaires \nest traité par la transformation en sommet et bissectrice TSB, capable de détecter précisément \nles bissectrices et les sommets d’un triangle. Les performances de la TCB et de la TSB sont \nestimées sur plusieurs bases d’images de scènes urbaines.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".