Development of Emergency Landing Algorithm for Drone and UAM Based on Visual Image Processing

In this study, we developed an algorithm that utilizes drone footage to construct training data, applies deep learning techniques for object inference, identifies suitable landing zones, and designates these zones as emergency landing sites. In particular, we developed a drone-based algorithm using YOLO-Seg deep learning to identify emergency landing zones for drones and UAM vehicles. By incorporating diverse training data, the algorithm segments landing areas with an accuracy of 84%-98% under various conditions. For the training process, we employed the YOLO-Segmentation model, which is a real-time object detection algorithm, to achieve precise boundary segmentation for landing zones. This segmentation facilitates accurate area detection and validates the performance of the algorithm. Furthermore, training data captured at different times of the day were incorporated to account for various environmental factors, thereby enhancing the robustness of the algorithm. This versatile algorithm can be applied to a wide range of aerial mobility safety scenarios. The proposed algorithm has demonstrated application potential in diverse and extensive scenarios related to aerial mobility safety.