The development of UAV technology has reached the stage of implementing artificial intelligence, control, and sensing. Cameras as UAV data inputs are employed to ensure flight safety, search for missing persons, and disaster evacuation. Human detection using cameras while flying is the focus of this article. The application of human detection in pedestrian areas using aerial image data is used as the dataset in the deep learning input process. The architectures discussed in this study are YOLOv5 and YOLOv8. The precision, recall, and F1-score values are used as comparisons to evaluate the performance of these architectures. When both architecture performances are applied, YOLOv8 outperforms YOLOv5. The achieved performance of YOLOv8 is a precision of 84.62%, recall of 75.93%, and F1-score of 79.98%.
Monitoring forests is one of the strategies in the overall preventive strategy. Monitoring the forest can quickly and permanently manage how tree illnesses emerge, spread, and evolve. To help monitor forest fires, a robot platform that can operate independently and assist in data collection can be created. In this paper, the accuracy of the Ardupilot-controlled autonomous navigation system of the rover was examined. The metode are used is experimental study, the study consists of a comparison between the GPS rover log and the SITL simulation within the mission planner tool. The average accuracy achieved by altering the route's distance and shape is 94.58%. The lengthy path may be the source of the rover's inaccurate autonomous navigation. In this case, the turning angle problem has no real effect on how well and accurately the rover navigates on its own.
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