Multimodal Unmanned Aerial Vehicles Classification Using Vision and Acoustic Inputs
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) are started to use in military zones during wars, which creates fears regarding safety, privacy, and national security. In order to track their utilization effectively, we require effective mechanisms to identify and classify UAVs. Classical camera-based vision detection is poor in low light, fog, as well as under vision obstacles. It also subsists with vision since the UAVs emanate distinguishable sound patterns used for identification. A framework is proposed for the classification of UAVs that incorporates both vision as well as audio data. We worked on the dataset Roboflow, comprised of five classes: drone, helicopter, aircraft jet, bird, and plane. We worked on the YOLOv8 [1], ResNet18 CNN, as well as the Vision Transformer. ResNet18 CNN obtained the best visual accuracy, reaching 98.44%, followed by satisfactory results from YOLOv8 and ViT. We utilised the Drone AudioDataset for audio analysis. It comprised the CNN, the LSTM, as well as the Vision Transformer, where the ViT achieved the best precision, 95.81%. The results show that combining optical and acoustic techniques improves detection reliability, especially in challenging real-time conditions. Ongoing research will be directed towards building derivative fusion techniques to construct variable, accurate, and secure UAV surveillance platforms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".