Real‐time CVSA decals recognition system using deep convolutional neural network architectures
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
Abstract The Commercial Vehicle Safety Alliance (CVSA) aims to achieve uniformity, compatibility and reciprocity of commercial motor vehicle inspections and enforcement by certified inspectors dedicated to driver and vehicle safety. Commercial vehicles that pass a CVSA inspection are eligible for a decal representing a commitment to safety. In this work, we propose a two‐step automatic CVSA decal recognition system using deep convolutional neural network architectures. The first step localizes a vehicle's windshield and the CVSA decal within, and classifies the decal colour. The CVSA decal is cropped and used as input to the second stage, which localizes and classifies a digit and the corner‐cut of a CVSA decal. With the corner cut, colour, and digit, the system can determine the decal's date of issue. We use as our baseline the MobileDet architecture, customizing the backbone to our tasks. Our first custom architecture is larger than the baseline because it needs more representational power to detect small decals within an image. The second architecture is much smaller because digit and corner‐cut recognition is a simpler task. Our custom architectures reduce processing time and exceed accuracies relative to pre‐trained architectures. We implemented our models on different edge hardware accelerators (e.g. the Google Coral, Nvidia Jetsons, and Intel NCS) and compared the performance when processing a real‐time video stream. Our system can predict frames at 173.31 FPS on an NVIDIA Jetson AGX Xavier with 98.5% mean average precision @ 0.5 IoU.
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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) | 0.001 | 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