Scientific Development Research

This research focuses on the autonomous obstacle avoidance algorithm of unmanned aerial vehicles (UAVs). The advanced model YOLOv10 in the field of deep learning is selected as the core detection algorithm, aiming to improve the obstacle avoidance efficiency and flight safety of UAVs in complex environments. Combined with the technology of binocular vision ranging, the precise detection and three-dimensional positioning of obstacles based on YOLOv10 are achieved. At the same time, the coordinate transformation relationship and the optimal approximate solution of the three-dimensional coordinate of obstacles are derived. The algorithm design covers key steps such as image pre-processing, model inference, obstacle detection, and obstacle-avoidance strategy formulation. Experiments use the Open Image dataset and custom images to evaluate the performance of YOLOv10 under the condition of various lighting, blocking, and speeding. The results show that YOLOv10 exhibits high accuracy, high obstacle-avoidance success rate, and good adaptability to lighting and blocking in UAV obstacle-avoidance tasks, and its performance is stable during high-speed flight. This research provides new ideas and methods for algorithm optimization in related fields and has important theoretical and practical significance.

Autonomous obstacle avoidance of UAVs; YOLOv10; Deep learning; Binocular vision ranging