Real-Time Binary Cell Phone Usage Detection and Classification on Vehicular Edge Devices
Bibliographic record
Abstract
IoT binary classification tasks can benefit significantly from edge computing because it allows for real-time processing and decision-making. By gathering and processing data locally, edge devices can lower latency and enable quicker response times, which is beneficial for applications whose main aims are safety and security. Cell phone usage while driving is one of the worst scenarios that decreases traffic safety and causes accidents. A wide range of new applications and services could be possible with the convergence of IoT and cell phone detection in the car while in driving mode. Machine learning methods, which include the ability to track people and objects in realtime, increase public safety by identifying and preventing potential security threats and improve transportation system efficiency by streamlining traffic and easing congestion. Although object detection is the most common approach for cell phone detection, the binary classification approach has been proposed because of its fast processing ability and easy deployment on edge devices. The device, in consideration, incorporates an inside camera to gather driver image data to perform binary classification. After collecting images from edge devices, these data are prepared in detail in an IID (independently and identically distributed) manner for better training for deep learning models. After training, test results are obtained by interpolation and extrapolation analyses. Results show that interpolation accuracy increases by $1.1 \%$ and extrapolation accuracy increases by $16.5 \%$.
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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 | 0.000 | 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 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".