针对基于单目视觉的无人机（UAV）避障问题，本研究提出基于单目深度估计和目标检测的四旋翼自主避障方法。其中，单目深度估计模型提供障碍物像素级别的深度信息，目标检测模型提供障碍物的位置信息。单张红绿蓝（RGB）图像的深度图和目标检测结果由卷积神经网络（CNN）获得；图像的区域划分以目标检测结果为依据，区域深度以深度估计结果为计算依据；规划算法依据区域深度和区域划分结果计算无人机的线速度和角速度，实现无人机的自主避障。为验证算法的自主避障性能，采用Parrot Bebop2无人机对本研究提出的算法与直飞算法进行实飞对比实验。结果表明：本研究提出的算法可用于四旋翼无人机的低速自主避障。

In order to solve the obstacle avoidance problem of unmanned aerial vehicle (UAV) based on monocular vision, a quadrotor autonomous obstacle avoidance method based on monocular depth estimation and object detection was proposed.The monocular depth estimation model provides the pixel-level depth information of the obstacle, and the object detection model provides the location information of the obstacles.The depth map and object detection results of a single Red-Green-Blue image were obtained by convolutional neural network (CNN).The region division of the image was based on the object detection results, and the region depth was calculated based on the depth estimation results.The linear velocity and angular velocity of UAV were calculated by the planning algorithm based on the regional depth and regional division results, so as to realize the autonomous obstacle avoidance of UAV.In order to verify the autonomous obstacle avoidance performance of the algorithm, the Parrot Bebop2 UAV was employed to carry out real flight comparison experiments between the proposed algorithm and the direct flight algorithm.The results show that the proposed algorithm can be used for low speed autonomous obstacle avoi-dance of quadrotor.