Unmanned Aerial Vehicles (UAV) has become ubiquitous. While there are several applications for UAV, it is also considered as a threat to the privacy and physical security. In this work we attempt to detect the invading UAV's with a goal of disabling them when they are invading a physical space. Identification of the UAV is performed by analyzing the live video feeds from cameras that are from Fixed CCTV cameras and surveillance drones. We propose to use image processing using convolutional neural network (CNN) for detecting the presence of the drones. Once the invading drone is identified, the information is sent to the Signal Jammer system. Our prototype shows very promising results that encourages us to pursue in building a real-world system.
N. Shijith, Poornachandran, P., Sujadevi, V. G., and Meher Madhu Dharmana, “Breach detection and mitigation of UAVs using deep neural network”, in 2017 Recent Developments in Control, Automation Power Engineering (RDCAPE), Noida, India, 2017.