Real time Detection and Tracking of the Target Object using the Yolov3 Network by the Flying Robot

In this paper, artificial intelligence is applied for real-time object detection in Tello quadcopters. For this purpose, the YOLOv3 detection algorithm is employed. The results indicate that the YOLOv3 network can detect the target with above 95 percent accuracy at a speed of 15 frames-per-second for different ambient lighting and background conditions. After detecting the target, the errors are calculated and given to the control system to track the target in real-time. The designed controllers efficiently follow the target and prevent flying robots from losing sight of the target.