Edge AI-Enabled Radar and Camera Integration for Real-Time Drone Detection and Classification
Bibliographic record
Abstract
This work presents a real-time, edge-deployable AI framework for drone detection and classification using fused radar and EO/IR sensor data. The system was validated through extensive field trials across varying environmental and operational conditions, including both daytime and night operations. The collected dataset comprises synchronized radar tracks, visible-spectrum video, and thermal imagery of UAVs performing maneuvers such as payload delivery at ranges up to 1 km. A latefusion architecture was implemented to combine radar-derived motion features with EO/IR visual cues, resulting in improved classification stability and robustness. The system achieved a radar-based UAV classification accuracy of 94%, and the EO/IR detection model reached a mean average precision (mAP@0.5) of 0.809. All modules were deployed on an NVIDIA Jetson Orin Nano, sustaining over 60 FPS at under 15W power consumption, with no cloud or uplink dependency. These results demonstrate the feasibility of deploying multi-sensor drone perception frameworks on low-power platforms for autonomous, real-time security and airspace monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".