Drones have recently occupied a central position in the operations of many businesses and governmental bodies.From quick deliveries at rush hour to scanning an unreachable military base, drones are proving to be very useful in areas that humans cannot access or where they are unable to complete tasks quickly and effectively. But with their advancement, drones are turning out to be a huge security threat. In order to distinguish between drones and other flying objects such as birds, kites, airplanes, and more in a variety of light and weather conditions and distances, this paper suggests a model based on deep learning. A custom dataset and YOLOv5 have been used to achieve the above parameters. For user friendly access the paper proposes a GUI which helps to deploy the model on images, videos and real-time feed

convolutional neural network, custom dataset, deep learning,drone detection, graphical user interface, object detection, YOLOv5