Deep4Air: A Novel Deep Learning Framework for Airport Airside Surveillance

TL;DR

Deep4Air is a deep learning framework that enhances airport ground surveillance by providing real-time aircraft detection, tracking, and speed analysis, improving safety and operational efficiency.

Contribution

It introduces a novel adaptive deep neural network for real-time aircraft detection, tracking, and analytics in complex airport environments.

Findings

Detection and tracking accuracy up to 99.8% on simulated data

High precision in aircraft localization relative to infrastructure

Effective real-time monitoring of aircraft speed and separation

Abstract

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.