Deep Learning Models for Gesture-controlled Drone Operation
Why this work is in the frame
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Bibliographic record
Abstract
Recently Unmanned Aerial Vehicles (UAVs) or Drones have gained enormous attention in applications like military, agriculture, industry, etc. One approach of controlling the operation of a drone is using hand gestures, which enables designing a low-cost system. However, the accuracy of such a system highly depends on the gesture recognition models. We can use a neural network-based gesture recognition model, which is a widely accepted image recognition scheme. In this work, we first design three deep neural network-based gesture recognition models: simple Convolutional Neural Networks (CNN), VGG-16, and ResNet-50 to uncover the best model for drone control. We evaluate the proposed models over our generated hand-gesture images in terms of their accuracy, precision, and complexity. The analysis reveals that each of the three models has its advantages and disadvantages while balancing between accuracy and complexity. For example, Simple CNN offers 92% accuracy on the testing set validation with the lowest validation loss compared to VGG-16 and ResNet-50. Thus, users can choose one of the proposed models to match their drone application.
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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 | 0.000 |
| 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 it