Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha
Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha
Faculty Publications
The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). …
Ads-B Classification Using Multivariate Long Short-Term Memory–Fully Convolutional Networks And Data Reduction Techniques, Sarah Bolton *, Richard Dill, Michael R. Grimaila, Douglas Hodson
Ads-B Classification Using Multivariate Long Short-Term Memory–Fully Convolutional Networks And Data Reduction Techniques, Sarah Bolton *, Richard Dill, Michael R. Grimaila, Douglas Hodson
Faculty Publications
Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of …