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.