Simulated Dataset for the Loaded vs. Unloaded UAV Classification Problem Using Deep Learning
Bibliographic record
Abstract
Detecting payloads on Uncrewed (or Unmanned) Aerial Vehicles (UAVs) is crucial for safety and security reasons. Deep learning methods can utilize changes in UAV appearance caused by payloads for detection, but collecting sufficient training data through real tests is costly and time-consuming. Therefore, simulation can be a more practical option. This paper presents the first synthetic air-to-air vision dataset for classifying loaded vs. unloaded UAVs. The dataset includes five types of aerial vehicles with attached and hanging payloads of different colors. It also incorporates three environmental conditions (sunny, rainy, and snowy) to diversify the background in recorded videos. Annotated frames and XYZ coordinates of the camera and drone are provided. To validate the dataset, a ResNet-34 network is trained with synthetic data and tested on real UAV flight data. The classification results on the test dataset confirm the effectiveness of the synthetic dataset for payload detection. The synthetic datasetandclassificationcodes arepublicly available on GitHub (https://github.com/CARG-uOttawa/loaded-unloaded-drone-dataset/).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".