10.25394/PGS.7492472.v1 Hyun Hwang Hyun Hwang Low-Cost and Scalable Visual Drone Detection System Based on Distributed Convolutional Neural Network Purdue University Graduate School 2018 Deep Learning Convolutional Neural Network Drone Detection Computer Vision Low-Cost Artificial Intelligence and Image Processing 2018-12-20 16:01:34 Thesis https://hammer.purdue.edu/articles/thesis/Low-Cost_and_Scalable_Visual_Drone_Detection_System_Based_on_Distributed_Convolutional_Neural_Network/7492472 <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>