YOLOV3 based Real Time Drone Detection for Counter Drone System

Nowadays, surveillance across the borders is a major issue since the dropping and supply of ammunition, explosives, and weapons is done using drones. In this paper, an implementation of YOLO based drone detection and recognition was implemented for surveillance applications. Drones are unmanned aircraft that may be remotely piloted (ground control) and come in a variety of sizes and configurations for use in a variety of applications. Lawbreakers employ drones for unlawful operations despite restrictions on their use in sensitive areas being in place because they are inexpensive and easy to use. To detect drones in real time and with high accuracy, a deep learning-based object detection model (YOLOv3) is trained on a specific dataset in this study.