A monocular vision obstacle avoidance method for quadrotor based on deep learning was proposed to help quadrotors to avoid obstacles. Firstly, the position of object in the image was obtained by object detection, and by calculating the height of the object box in the image, the distance between quadcopter and obstacle was estimated. Then, whether performing obstacle avoidance was determined by synergetic computer. Finally, experiments were conducted on a flight test platform based on Pixhawk flight control board. The results show that the proposed method can be applied to quadcoptor obstacle avoidance with low speed. Compared with traditional active sensor methods, the proposed method greatly reduces the occupied volume with only one monocular camera as sensor. This method is robust and can identify people with different postures as obstacles.