Integrating Visual Geometry and Mask Region CNN for Enhanced UAV Detection and Identification
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have been adopted in various applications, including agriculture, public safety, surveillance, and crucial military missions. However, alongside their advantageous nature, UAVs have also been employed for malicious activities, leading to an increased requirement for timely detection and identification. Despite significant progress in UAV detection, challenges persist, particularly concerning various types of UAVs, the payload carried by UAVs, and the traits of their flight. Employing single machine learning for detection and identification has limitations due to the inability to handle diverse datasets and acquire complex relationships. Therefore, in this paper, we introduce a novel integration of the Visual Geometry Group-based convolutional neural network (VGG-CNN) framework employed for detection with the Mask Region-based convolutional neural network (MR-CNN) for identification of UAVs (jointly termed MR-DCNN). For efficient deployment of MR-DCNN, we add diversity to the dataset by performing data augmentation of new images in the training dataset for the detection of various types of UAVs, payload categories, and flight characteristics. The performance evaluation of the MR-DCNN approach was conducted via simulations, revealing superior detection capabilities for malicious UAVs compared to existing methods.
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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 | 0.000 |
| 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".