Face Mask Detection: A Real-Time Android Application Based on Deep Learning Modeling
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
The accelerated spread of the COVID-19 (coronavirus) disease has put stress on healthcare systems. Some safety measures are provided, such as keeping social distance and wearing a mask, which can help curb transmission and save lives. This paper aims to detect whether a person is wearing a mask or not with video surveillance to enforce health and safety regulations in real-time. We propose a solution for face mask detection using two deep learning models, the MobileNetV2 and the Modified Convolutional Neural Network (MCNN). The trained models are converted to TensorFlow Lite to deploy an Android Application. Our models can achieve up to 99% accuracy. In this paper, an analysis of the number of individuals not wearing masks is provided by capturing the face and storing it on a mobile-backend-as-a-service. Our application can be adopted to increase health measures in real-time and control the spread of COVID-19.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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