This invention relates to the field of
image processing technology, and more particularly to a method for local
horizon positioning of unmanned aerial vehicles (UAVs). The method utilizes multiple miniature cameras mounted on the UAV to capture surrounding images and extracts a preliminary
skyline from these images. Then, based on the preliminary
skyline, an energy function of the surrounding images is calculated, and the preliminary
skyline is optimized using this energy function to obtain a complete skyline. The surrounding images are then divided into
sky and ground regions using the complete skyline. These two regions are input into a trained
convolutional neural network model to obtain the UAV's position coordinates relative to the complete skyline. This invention extracts the skyline from the image by calculating the edge response of the image and combining this with an energy
function optimization method. Then, a lightweight environment recognition neural
network model is used to perform collaborative environment-level recognition of the
local environment, avoiding the limitations of feature-level and target-level recognition methods that are confined to local image information. This achieves efficient positioning of UAVs in local environments.