Vera-Yanez Daniel, Pereira António, Rodrigues Nuno, Molina José Pascual, García Arturo S, Fernández-Caballero Antonio
Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.
Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic Institute of Leiria, 2411-901 Leiria, Portugal.
Sensors (Basel). 2024 May 9;24(10):3016. doi: 10.3390/s24103016.
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.
天空看似足够广阔，两架飞行器不会相撞，但事实表明，空中碰撞仍偶尔发生，且是一个重大问题。飞行员学习手动策略以避免碰撞，比如“看到并避开”，但这些规则存在局限性。自动化解决方案减少了碰撞，但这些技术并非在所有国家或空域都是强制性的，而且成本高昂。这些问题促使研究人员继续寻找低成本解决方案。一个有吸引力的解决方案是使用计算机视觉来检测空中的障碍物，因为其成本和重量较低。一个训练有素的深度学习解决方案很有吸引力，因为在大多数情况下目标检测速度很快，但它完全依赖于训练数据集。本研究选择的算法是光流。光流矢量可以帮助我们在不依赖训练数据的情况下，将由相机运动引起的运动与由来袭物体引起的运动区分开来。本文描述了一种基于光流的机载障碍物检测算法的开发，以避免空中碰撞。该方法利用单目相机的视觉信息，通过形态学滤波器、光流、扩展焦点和数据聚类算法来检测障碍物。使用通过自行开发的模拟器获得的逼真视觉数据对该提议进行了评估。该模拟器提供了不同的飞行物体环境、轨迹和高度。结果表明，基于光流的算法在实验中沿着轨迹检测到了所有来袭障碍物。结果显示F分数大于75%，并且在精确率和召回率之间取得了良好的平衡。
