To address the challenges of high computational complexity and poor real-time performance in binocular vision-based Unmanned Aerial Vehicle (UAV) formation flight, this paper introduces a UAV localization algorithm based on a lightweight object detection model. Firstly, we optimized the YOLOv5s model using lightweight design principles, resulting in Yolo-SGN. This model achieves a 65.5% reduction in parameter count, a 62.7% reduction in FLOPs, and a 1.8% increase in accuracy compared to the original detection model. Subsequently, Yolo-SGN is utilized to extract target regions from binocular images, and feature point matching is exclusively conducted within these regions to minimize unnecessary computations in non-target areas. Experimental results demonstrate that the combination of Yolo-SGN and the Oriented FAST and Rotated BRIEF (ORB) algorithm reduces feature point matching computations to only a quarter of those in the original ORB algorithm, significantly enhancing real-time performance. Finally, the extracted feature points from UAVs are input into a binocular vision localization model to compute their three-dimensional coordinates. The average of the three-dimensional coordinates of all feature points is used to determine the three-dimensional position of the target UAV. Experimental results confirm that the UAV binocular vision localization algorithm, based on a lightweight object detection model, exhibits exceptional performance in terms of precision and real-time capabilities.
Keywords: Binocular stereo vision; Deep learning; Lightweight network; Unmanned aerial vehicle detection.
© 2025. The Author(s).