GA-Based Optimization of Visible and Infrared Decision-Level Data Fusion for UAV Detection
Bibliographic record
Abstract
This paper presents the development of a framework for the detection of small non-cooperative Unmanned Aerial Vehicles (UAVs) using Electro-optical (EO) and Infrared (IR) sensors, in the context of Counter-Unmanned Aerial Systems (C-UAS). First, the hyperparameter tuning procedures for the training of the YOLOv7 object detector using spatial and temporally aligned datasets of EO and IR images of UAVs are described. The optimization is performed using a Genetic Algorithm (GA), and elitism and the use of a reduced number of images are implemented to improve the rate of convergence. Using the optimized EO and IR models, a decision-level data fusion approach for UAV detection is implemented and tested using the spatial and temporally aligned test sets and inference videos. Results show notable improvements in metrics, with maximum precision and recall increases of $10.9 \%$ and $5.1 \%$, respectively, with a minor compromise on frame rate.
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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 | 0.001 |
| 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".