Selective Cloud Offloading for Accurate and Efficient Object Detection
Bibliographic record
Abstract
High-accuracy object detection on resource-constrained devices is becoming increasingly important for applications in autonomous systems, smart surveillance, and mobile computing. However, deploying high-performance object detection models on these devices is impractical due to computational limitations, and transmitting and processing all data on a much more powerful remote server running significantly more complex and accurate models, known as full cloud offloading, incurs high latency and is constrained by network bandwidth. In this paper, we propose a selective cloud offloading framework that provides users with control over the tradeoff between prediction accuracy and processing cost. Our approach employs a lightweight object detection model on the edge to make initial predictions, leveraging conformal prediction to quantify uncertainty. Only high-uncertainty regions are offloaded to the cloud, where more powerful models refine predictions, improving overall detection accuracy. To further optimize efficiency, multiple uncertain regions are combined into a single image before offloading, reducing transmission and processing costs. We present the architecture of our system and evaluate its performance on real datasets, demonstrating that it achieves cloud-level accuracy while significantly reducing offloading overhead.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".