Drone Detection using YOLO and SSD A Comparative Study

Unmanned Ariel Vehicles (UAVs) are one of the fastest-growing technologies with a wide range of uses. UAVs have become more accessible to the masses over the recent years. However, both negligent and malevolent usage of UAVs can pose a significant risk. Drone technology today poses a catastrophic threat to the governments, companies, and the general public. Drones have been used for espionage and military strikes in the past, and as the market for UAVs grows, more of these attacks can be expected in the future. Combat drones or UCAVs have changed how dangerous operations were earlier carried out. They can also be used to remotely launch targeted attacks or transport heavy weaponry. For this reason, it is essential for an organization to adopt automated drone detection and tracking mechanisms. Ultra-high-definition radar, microphones, cameras, and radio frequency (RF) sensing are among the technologies being considered and used for UAVs. Such detecting methods, on the other hand, entail substantial maintenance and configuration expenses. Because they aren't portable, they can't be implemented on a broad scale. The advancements in GPU technology have given a huge boost to deep learning; a field which wasn't considered to be feasible due to its high computing power requirements. Deep Learning has given rise to many new areas of research; especially Computer Vision (CV).