Edge TMS: Optimized Real-Time Temperature Monitoring Systems Deployed on Edge AI Devices
Bibliographic record
Abstract
Temperature monitoring system (TMS) aims to reduce the infection spread and outbreak of COVID-19 through early detection. Conventional and currently deployed TMS have high implementation cost and require a substantial amount of space. Also, the performance often depends on the accuracy of the thermal camera. To address this, we propose Edge TMS wherein a multi-task cascaded convolutional neural networks (MTCNN)-based TMS is deployed on an edge AI device. To overcome the resource constraints of edge AI devices, an optimization method is applied to compress MTCNN up to 100×. The compressed MTCNN is deployed on the local PC, Jetson Xavier, Jetson TX2, and Jetson Nano which yields a Pruning-Per-Reduction Ratio (PPRR) values of 1.21, 1.63, 1.99, and 2.10, respectively. We proposed the PPRR metric to measure the performance of the compressed model. Low PPRR values indicate an improvement in the hardware performance and computational efficiency of the optimized model. The optimized model deployed in all the Jetson series achieved an average percent power reduced (%R) of 53.18% with a percent difference of 35.9% from the results of the local PC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".