Currently, as one of the most popular technologies being discussed and experimented, the application of flying copters in different industries is facing an obvious barrier; which is how to avoid obstacles while flying. One of the industries among all is small-sized package delivery business, which is also the master topic of a series of experiments. The most popular designs that have used for the Flying Copter Obstacle Avoidance System such as lidar scanners and infrared rangefinders are significantly accurate. However, with the heavyweight, expensive price and higher power consumption, these systems cannot be put into mass production. To reduce the cost and power consumption of the Obstacle Avoidance System, an innovative vision-based low-cost Obstacle Distance Estimation System for flying copters is demonstrated in this thesis. The Fisheye Lens Camera is used to provide a broader detection range and accurate results. Compared to other standard vision-based systems, the Fish Lens Camera Distance Estimation System can provide (around 360 degrees) extensive view for obstacle detection. Through the parallax pictures captured by the camera and the trigonometric rules, the system can estimate the distance to the target obstacle with reasonable results.