Secure Vision-Based Navigation for Drones in GPS-Denied Environments Using Machine Learning
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) have become indispensable across domains such as surveillance, disaster management, and industrial inspection. However, their heavy reliance on Global Positioning System (GPS) signals exposes them to spoofing, jamming, and denial-of-service attacks, creating significant risks in critical operations. To address this limitation, we propose a secure vision-based navigation framework that integrates Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and efficient path-planning algorithms. A lightweight UNet architecture with a ResNet50 backbone was trained on aerial datasets, achieving a Dice coefficient of 0.9155 and a mean Intersection over Union (mIoU) of 0.8658, enabling robust segmentation of roads, buildings, terrain, and obstacles. SLAM, powered by SuperPoint feature detection and SuperGlue matching, demonstrated reduced drift and improved trajectory consistency compared to classical ORBbased methods. Path-planning experiments further showed that A* consistently outperformed Dijkstra in real-time navigation scenarios. The integration of CNN-driven perception with SLAMbased localization provides reliable cost-fused maps for autonomous decision-making. This framework enhances confidentiality, integrity, and availability in UAV navigation, offering a scalable GPS-independent solution for secure deployment in contested and GPS-denied environments.
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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 | 0.001 | 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".