An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration

Li, Bo; Liang, Shiyang; Tian, Linyu; Chen, Daqing; Zhang, Ming

doi:10.1155/2020/5391479

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…2 Swarms of UAVs are now being deployed, which operate using a shared network intelligence, coordinate with one another to solve a set of tasks. 3 UAVs have tremendous potential in various applications. However, their increasing use and popularity raises security concerns, and automated methods for identification and tracking are needed.…”

Section: Introductionmentioning

confidence: 99%

DyViR: dynamic virtual reality dataset for aerial threat object detection

Williams,

Lecakes,

Almon

et al. 2023

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications

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Unmanned combat aerial vehicles (i.e., drones), are changing the modern geopolitical stage's surveillance, security, and conflict landscape. Various technologies and solutions can help track drones; each technology has different advantages and limitations concerning drone size and detection range. Machine learning (ML) can automatically detect and track drones in real-time while superseding human-level accuracy and providing enhanced situational awareness. Unfortunately, ML's power depends on the data's quality and quantity. In the drone detection task scenario, limited datasets provide limited environmental variation, view angle, view distance, and drone type. We developed a customizable software tool called DyViR that generates large synthetic video datasets for training machine learning algorithms in aerial threat object detection. These datasets contain video and audio renderings of aerial objects within user-specified dynamic simulated biomes (i.e., arctic, desert, and forest). Users can alter the environment on a timeline allowing changes to behaviors such as drone flight patterns and weather conditions across a synthetically generated dataset. DyViR supports additional controls such as motion blur, anti-aliasing, and fully dynamic moving cameras to produce imagery across multiple viewing angles. Each aerial object's classification (drone or airplane) and bounding box data automatically exports to a comma-separated-value (CSV) file and a video to form a synthetic dataset. We demonstrate the value of DyViR by training a real-time YOLOv7-tiny model on these synthetic datasets. The performance of the object detection model improved by 60.4% over its counterpart not using DyViR. This result suggests a use-case of synthetic datasets to surmount the lack of real-world training data for aerial threat object detection.

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Section: Introductionmentioning

confidence: 99%