Drone Recognition Using YOLOv7
Drones are widely used to serve various purposes in industry, mapping, and crisis management. However, the use of drones is increasing in important places such as airports, power plants, and important buildings, endangering public safety and causing problems with aircraft. For this reason, it is very important to detect and recognize them accurately and quickly to prevent security problems caused by unauthorized access to critical infrastructures. In this project, a novel and accurate technique based on deep learning networks is presented to detect and recognize 3 types of flying drones in visible images and differentiate them from birds using a set of 26,000 normal visible images.
METHOD
In this project, to achieve higher accuracy in solving the challenges of drone type recognition in visible images, YOLOv7 deep learning network is used. Drone recognition challenges include the drone’s far distance from the camera, a crowded background, unpredictable movements, and the drone’s resemblance to birds. As an independent approach, the YOLOv7 deep learning network architecture is capable of recognizing birds and three types of drones: multirotors, fixed-wings, and helicopters.
Diagram
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