CycleGAN-Assisted Domain Adaptation for UAV Payload Detection
Bibliographic record
Abstract
As uncrewed aerial vehicles (UAVs) become increasingly integrated into various domains, concerns about their potential misuse have grown, highlighting topics such as reliable detection of carrying payloads. This paper presents a novel twostage deep learning framework for detecting loaded UAVs using vision-based classification. Due to the high cost of collecting realworld training data, we utilize a synthetic dataset for model training. However, domain shifts between synthetic and realworld data can degrade classification performance. To address this challenge, we employ a CycleGAN-based domain adaptation method that transforms real test samples into their synthetic counterparts, ensuring consistency with the training distribution. The adapted samples are then classified using a pre-trained deep network based on ResNet and EfficientNet architectures. By preserving the integrity of the classifier while bridging the domain gap, our approach significantly improves UAV payload detection. Experimental results demonstrate the effectiveness of the proposed method in enhancing classification accuracy for real-world UAV monitoring applications.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".