A computer vision framework using Convolutional Neural Networks for airport-airside surveillance. (April 2022) - Thai, Phat
Abstract: Modern airports often have large and complex airside environments featuring multiple runways, with changing configurations, numerous taxiways for effective circulation of flights and tens, if not hundreds, of gates. With inherent uncertainties in gate push-back and taxiway routing, efficient surveillance and management of airport-airside operations is a highly challenging task for air traffic controllers. An increase in air traffic may lead to gate delays, taxiway congestion, taxiway incursions as well as significant increase in the workload of air traffic controllers. With the advent of Digital Towers, airports are increasingly being equipped with surveillance camera systems. This paper proposes a novel computer vision framework for airport-airside surveillance, using cameras to monitor ground movement objects for safety enhancement and operational efficiency improvement. The framework adopts Convolutional Neural Networks and camera calibration techniques for aircraft detection and tracking, push-back prediction, and maneuvering monitoring. The proposed framework is applied on video camera feeds from Houston Airport, USA (for maneuvering monitoring) and Obihiro Airport, Japan (for push-back prediction). The object detection models of the proposed framework achieve up to 73.36% average precision on Houston airport and 87.3% on Obihiro airport. The framework estimates aircraft speed and distance with low error (up to 6 meters), and aircraft push-back is predicted with an average error of 3 min from the time an aircraft arrives with the error-rate reducing until the aircraft's actual push-back event. Highlights: This paper proposes a computer vision airport ground movement surveillance framework. This is the first surveillance system that monitors runway to apron using CV models. The proposed framework is designed for the unique characteristics of airports. The framework outperforms state-of-the-art models Yolov3, Yolov4 and EfficientDet. A novel camera calibration technique is developed to transform pixels to locations. An algorithm is developed to continuously monitor and predict push-back time.