Real Time Drone Detection System

The development of drone technology over the past few years has given drones the potential to carry out much more complicated tasks on their own thanks to the integration of artificial intelligence, computer vision, and object avoidance technologies. However, the improper usage of drones, such as the drone incident at Gatwick Airport, led to significant disruptions that affected about 140,000 travellers. Drone surveillance is absolutely essential to prevent this from happening again. This will be accomplished by first detecting drones, then tracking them. The usage of a deep learning object detector called YOLOv3 with pretrained weights and transfer learning to train YOLOv3 to specifically recognize drones is presented and examined in this research. We showed how computer vision can be used in accomplishing this desired result in real time.