System for Tracking Aircraft Ground Movement Utilizing Artificial Intelligence

Abstract

Using a virtual ATC tower is one aspect of the airport digitization initiative, which aims to cut costs. Technology for tracking and detecting aircraft is required to guarantee the security of the deployment of virtual ATC towers. The invention of visual artificial intelligence that can recognize and follow aircraft ground movement in an airport is presented in this study. One feature that should be developed in this study is the capacity to issue a warning if two aircraft are in close proximity to one another. The techniques consist of a coordinate system conversion from pixels to meters for aircraft separation computation, the Deep SORT object tracking algorithm, and the YOLOv4 object recognition algorithm that has been trained using Image Dehazing Filter. Next, a fair-condition recorded airport video is used to validate the model. With a mean average precision score of 95.92%, the trained YOLOv4 model was able to track every aircraft in the video, and with an error of 5.09%, the aircraft separation warning system functioned as expected. An airport’s possible use of an aircraft ground movement tracker was demonstrated by the constructed model.