Bayesian Optimization-Aided Hybrid Deep Learning Model for Lightweight UAV-Based Smoke Detection
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) play a crucial role in various applications, including detecting environmental hazards, e.g., wildfire smoke detection. However, the limited computational capabilities and battery life of UAVs present barriers to deploying complex artificial intelligence (AI) models onboard. To address this challenge, we propose a novel hybrid deep learning framework for UAVs to carry out light-weight yet efficient smoke detection. The framework combines a lightweight model for initial image assessment and a depth-wise model for selective processing of uncertain cases. Bayesian optimization is employed to determine the optimal threshold values for activating the depth-wise model, striking a balance between accuracy and computational efficiency. The proposed approach eliminates the need for cloud server connectivity, enabling onboard decision-making. Experimental results demonstrate that the hybrid framework achieves significant reductions in processing time and the number of calls to the depth-wise model while maintaining high accuracy. The framework’s adaptability and robustness make it suitable for real-time smoke detection applications in resource-constrained environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".