论文标题
Deep4air：机场航空监视的新型深度学习框架
Deep4Air: A Novel Deep Learning Framework for Airport Airside Surveillance
论文作者
论文摘要
机场跑道和滑行道（Airside）区域是一个高度动态且复杂的环境，在不同类型的车辆（速度和尺寸）之间的相互作用（在不同的可见性和交通状况下）。机场地面移动是被认为是安全至关重要的活动，安全分离程序必须由空中交通管制员（ATC）维护。带有复杂跑道taxiway系统的大型机场使用先进的地面监视系统。但是，这些系统具有固有的局限性和缺乏实时分析。在本文中，我们提出了一个新型的基于计算机的框架，即“ Deep4Air”，它不仅可以通过对飞机位置的跑道和滑行道的自动视觉监控来增强地面监视系统，还可以为飞机上的飞机和跑道上的实时速度和距离分析提供。拟议的框架包括一个自适应的深神经网络，用于有效检测和跟踪飞机。实验结果表明，在模拟数据中，平均检测和跟踪高达99.8％，并在乔治·布什洲际机场的数字塔上进行了监视视频的验证。结果还表明，“ Deep4Air”可以以高准确性定位相对于机场跑道和出租车基础设施的飞机位置。此外，实时监控飞机速度和分离距离，从而提供了增强的安全管理。
An airport runway and taxiway (airside) area is a highly dynamic and complex environment featuring interactions between different types of vehicles (speed and dimension), under varying visibility and traffic conditions. Airport ground movements are deemed safety-critical activities, and safe-separation procedures must be maintained by Air Traffic Controllers (ATCs). Large airports with complicated runway-taxiway systems use advanced ground surveillance systems. However, these systems have inherent limitations and a lack of real-time analytics. In this paper, we propose a novel computer-vision based framework, namely "Deep4Air", which can not only augment the ground surveillance systems via the automated visual monitoring of runways and taxiways for aircraft location, but also provide real-time speed and distance analytics for aircraft on runways and taxiways. The proposed framework includes an adaptive deep neural network for efficiently detecting and tracking aircraft. The experimental results show an average precision of detection and tracking of up to 99.8% on simulated data with validations on surveillance videos from the digital tower at George Bush Intercontinental Airport. The results also demonstrate that "Deep4Air" can locate aircraft positions relative to the airport runway and taxiway infrastructure with high accuracy. Furthermore, aircraft speed and separation distance are monitored in real-time, providing enhanced safety management.
