Adaptive Visual Obstacle Avoidance Algorithm for Aerial Drones Integrating Deep Learning and Spatial Geometric Transformations

This study proposes a new visual obstacle avoidance method for drones based on the YOLO and PnP algorithms. Most existing obstacle avoidance methods are based on sensors such as lidar and ultrasonic sensors, or visual obstacle avoidance methods based on monocular or binocular cameras. In contrast, this study only uses a monocular camera on a two-axis gimbal on an aerial drone to detect faces in the picture, protecting the human head, which is the object that may be the most harmful, with the lowest hardware and algorithm costs, and providing effective obstacle avoidance strategy support for the autonomous flight safety of aerial drones.