<div>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</div><div>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.</div><div><br></div><div><div>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.</div></div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7492472 |
| Date | 20 December 2018 |
| Creators | Hyun Hwang (5930672) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Low-Cost_and_Scalable_Visual_Drone_Detection_System_Based_on_Distributed_Convolutional_Neural_Network/7492472 |
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