Drones are pilotless aircrafts known as unmanned aerial vehicles (UAV), which can be employed in places where manned aircraft are too dangerous or challenging to fly. They are essentially flying robots that may be operated manually or rendered autonomous with the aid of flight plans controlled by software. They are propelled through the air by the Embedded Processor's processing of data from onboard sensors and the Global Positioning System (GPS). As time goes on, there is a growing desire for drone utilisation due to the vast array of useful and cutting-edge applications they provide. Regardless of the application for which they are utilised, drones encounter particular difficulties. Finding and avoiding items that cross their route while navigating is one of their biggest problems. In this study, we suggest employing pre-trained neural networks and deep learning to detect frontal objects. The drone's front-facing monocular camera captures picture frames, which are then analysed and input into the deep learning network for object recognition. Regarding accuracy and speed, a comparison of three deep learning algorithms is also taken into account.
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