Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones
Bibliographic record
Abstract
Drones play a crucial role in search-and-rescue missions by providing real-time information on areas of interest that are difficult to access or endangering to human rescuers. However, analyzing raw video feeds by human operators to detect objects of interest, such as vehicles or victims, becomes increasingly demanding as the mission duration increases. This underscores the need for embedded computer vision to reduce the operator’s cognitive load and enhance mission responsiveness. Towards this goal, we propose a low-latency object detection model based on YOLO, fitted to search-and-rescue missions, and able to process data coming from RGB and event cameras. We also propose a low-latency implementation on FPGA on board drones, achieving accurate detection in less than 20ms. Through a series of tests using a prototype drone, we highlight the features of the model and processing core that favor drone reactivity and operational autonomy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".