Real-time deep learning for moving target detection and tracking using unmanned aerial vehicle

Real-timeand-rescue. object detection and tracking are crucial for many applications such as observation and surveillance, and search-There have been many advancements in deep learning techniques for object detection and tracking due to the successful development of computing devices. Based on these ideas, the YOLO deep learning visual object detection algorithm was utilized to visually guide the UAV to track the detected target. The detected target bounding box and the image frame center were the main parameters that were used to control the forward motion, heading, and altitude of the vehicle. The proposed control system approach consisted of two PID controllers that managed the heading and altitude rates. For a real-time computing device a Nvidia Jetson TX2 based edge-computing module is used, whichh takes the input data from onboard sensors such as camera. A navigation system operated entirely onboard the UAV in the absence of external localization sensors or a GPS signal is introduced, and it uses a fisheye camera to perform a visual SLAM for localization. The robustness and effectiveness of the proposed deep-learning based target detection and tracking algorithms were verified through various simulation and real-time flight experiments.