A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

Gray consistency optical flow algorithm based on mask-R-CNN and a spatial filter for velocity calculation

The optical flow method has been widely used to measure the vehicle velocity by observing the stationary ground with a camera looking-down. However, when there are moving objects on the stationary ground, the interfering optical flow field will be generated, which decreases the velocity measurement accuracy of a vehicle relative to the ground. In order to reduce the effects caused by moving objects, this paper integrates pyramid Lucas-Kanade (LK) algorithm with the gray consistency method to use the information of color images thoroughly. First, a mask region with convolutional neural network (Mask-R-CNN) is used to recognize the objects that have motions relative to the ground, and it covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then images are decomposed into three channels, red, green, and blue (i.e., R, G, and B), and processed by median filter. Based on the gray consistency method, the optical flow can be obtained by the pyramid LK algorithm. Finally, the velocity is calculated by the optical flow value. The prominent advantages of the proposed algorithm are: (i) increase the velocity measurement accuracy of a vehicle relative to the ground; (ii) use the information of color images acquired with cameras thoroughly and obtain velocity calculation outputs with less fluctuation; (iii) reduce wrong values caused by noises that are from the origin image and introduced by similar color masks. Four experiments are conducted to test the proposed algorithm and results with superior precision and reliability show the feasibility and effectiveness of the proposed method for the velocity measurement accuracy of a vehicle relative to the ground.