Outdoor Check Stop HAZMAT Placard Detection Using Synthetic Images and YOLOv5-Small
Why this work is in the frame
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Bibliographic record
Abstract
Deep learning training is frequently supplemented with synthetic data. We propose a scheme to synthesize images of HAZMAT placards in outdoor environments (e.g., check stops) to train models for detection and classification. Our process has 3 levels of realism, with noise and other distortions, and can simulate day and nighttime images. We used our data to train YOLOv5-Small models and evaluated models over a test dataset of 4321 real outdoor check stop images containing 6738 placards across 16 classes. Respectively, the best models for similar and single-class placard groupings had 0.867 and 0.927 best half-precision test set mAP@0.5. Models run at 65 FPS on Nvidia’s AGX Xavier edge GPU single-board computer, and at 12 FPS on Nvidia’s Nano.
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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