ROBUST EXTRACTION OF TRAFFIC SIGNS FROM GEOREFERENCED MOBILE MAPPING IMAGES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern Driver Assistance Systems (DAS) are required to assist, guide, and control vehicles on highways and city streets based on GNSS, INS and map matching. They play an important role in modern vehicles navigation. Although a GNSS-navigation system can be updated in view of the modifications of the roads, it does not include exhaustive information about the traffic signalization. It would be useful to signal to a driver at least some important traffic signs. This paper presents the basic concept of a new approach for the automated detection of traffic signs to be incorporated in DASs. The developed procedure is based on the well known Scale Invariant Feature Transform (SIFT) algorithm. The results of extensive testing on real data sets show that the presented approach detects and classifies over 70% of traffic signs correctly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it