Deep learning based object detection via UAV and computation of GNSS location coordinates

Robotic systems nowadays are used in autonomous (unmanned) task applications. An Unmanned Aerial Vehicle (UAV) can be given as an example of robotic systems. UAVs, which are used in many applications in military and civilian fields, can perform many tasks such as obstacle avoidance, object recognition, autonomous target detection with the help of sensors with various technologies attached to them. Using deep learning and machine learning based on artificial intelligence algorithms are becoming increasingly common as image processing techniques in such tasks. In this study, a system is designed to calculate remotely the Global Navigation Satellite System (GNSS) coordinates of the selected object as a target via the UAV. The heart of the system is a 3-axis gimbal-mounted camera and laser distance measurement module (LiDAR). LiDAR is placed next to the camera on the gimbal and in the same direction. Live camera images and LiDAR distance data are transferred in real-time to the interface software. Possible targets in the image are framed by deep learning methods on the interface software. The user can move the gimbal system with the remote control to center the chosen target to the center of the screen. By pressing the button on the interface software, the GNSS coordinates, yaw angle and target distance of the UAV and yaw and pitch angles of the gimbal are arranged and sent to the Vincenty formula. The GNSS coordinates of the target are calculated. In order to test the performance of the system, the GNSS coordinates of the sample target objects were measured statically and compared with the coordinates calculated dynamically on the UAV. Distance and bearing angle, which are from the coordinate of the UAV during the measurement to statically measured and dynamically calculated coordinates, are found with the Haversine formula. The performance of the calculated coordinate was evaluated by the obtained yaw angle and the relative error percentage of distances. This study was supported by Marmara University Scientific Research Projects Coordination Unit (Project No: FEN-C-YLP-170419-0123).