Integrating Multi-Spectral Image Fusion and Path Planning Strategies for Effective Drone-Based Search and Rescue
Bibliographic record
Abstract
In this paper, we investigate the integration of computer vision (CV) methodologies and advanced path planning strategies to enhance Search and Rescue (SAR) missions employing Remotely Piloted Aircraft Systems (RPAS), commonly known as drones. Effective SAR operations require rapid and accurate identification of missing or endangered individuals, which requires timely medical assistance to mitigate long-term health consequences. Traditional imaging techniques, such as RGB (visual) and thermal imaging, each present unique strengths and limitations within SAR contexts. To determine the most effective approach, we evaluated RGB, thermal and multispectral image fusion image datasets using the YOLO algorithm and four distinct path planning algorithms to optimize SAR missions: Scanline Fill, Quadrant Scanline Fill, Geocentric Fill, and Random Fill. We define multi-spectral image fusion as overlaying RGB and thermal imagery, enabling a combined view that leverages both modalities. Through extensive simulations, we verified that the Quadrant Scanline Fill algorithm consistently outperforms others in speed and efficiency, making it the most suitable choice for directing RPAS in search operations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".