YOLO-R Computer Key Technology for Trackside Equipment Information Recognition and Detection
Bibliographic record
Abstract
With the rapid expansion of high-speed railway network, the real-time monitoring of trackside equipment becomes particularly important.To detect trackside equipment information more accurately, a YOLO-R algorithm grounded on the improved You Only Look Once v3 (YOLOv3) algorithm is proposed, and the trackside equipment identi ication and detection model is constructed.By introducing feature pyramid network and adaptive Bessel curve network, the new model can effectively identify and locate different types of trackside equipment such as switch machine, derailer, and shaft counter.The experiment indings denote that the new model is superior to the existing technology in all aspects of on-orbit equipment recognition and detection, the computer resource occupancy rate is only 22%, the image recognition accuracy rate is more than 98%, and the processing speed is up to 200 images/second.This research not only raises the automation level of trackside equipment monitoring, but also provides a powerful technology for railway safety operation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".