Low-Cost and Scalable Visual Drone Detection System Based on Distributed Convolutional Neural Network
Abstract
Recently, with the advancement in drone technology, more and more hobby drones are being manufactured and sold across the world. However, these drones can be repurposed for the use in illicit activities such as hostile-load delivery. At the moment there are not many systems readily available for detecting and intercepting those hostile drones. Although there is a prototype of a working drone interceptor system built by the researchers of Purdue University, the system was not ready for the general public due to its nature of proof-of-concept and the high price range of the military-grade RADAR used in the prototype. It is essential to substitute such high-cost elements with low-cost ones, to make such drone interception system affordable enough for large-scale deployment. This study aims to provide an alternative, affordable way to substitute an expensive, high-precision RADAR system with Convolutional Neural Network based drone detection system, which can be built using multiple low-cost single board computers. The experiment will try to find the feasibility of the proposed system and will evaluate the accuracy of the drone detection in a controlled environment.
Degree
M.Sc.
Advisors
Matson, Purdue University.
Subject Area
Aerospace engineering|Artificial intelligence|Computer science|Military studies|Robotics|Transportation
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