Embedded Implementation of Social Distancing Detector Based on One Stage Convolutional Neural Network Detector
Bibliographic record
Abstract
The recent COVID-19 is a very dangerous disease that intimidates humanity. It spreads very fast and many rules must be respected to reduce its prevalence. One of the most important rules is the social distance which means keeping a safe distance between two persons. A safe distance must be one meter or more. Respecting such rules in public spaces is a very challenging task that needs the assistance of artificial intelligence tools. In this paper, we propose a social distance detector using convolutional neural networks. The detector was based on the Yolo model with a custom-made backbone to guarantee real-time processing and embedded implementation. The backbone was optimized to make it suitable for embedded resources. The inference model was evaluated on the Pynq platform. The model was trained and fine-tuned using the MS COCO dataset. The evaluation of the proposed model proved its efficiency with a precision of 87.98% while running in real-time. The achieved results proved the efficiency of the proposed model and the proposed optimization for embedded implementation.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".