Real-time Object Detection and Localization for Airport Aprons

Airport aprons serve as central hubs for various airport operations, including aircraft parking, refueling, maintenance, passenger boarding and deboarding, as well as cargo loading and unloading. Accurate and reliable monitoring of these operations is critical for maintaining operational efficiency and safety. However, airport apron environments are highly dynamic and complex, with aircraft, ground equipment, and personnel in constant motion. The FastGate project aims to develop a digital twin of the apron for monitoring and optimization purposes. The state of the art of the object detection and localization methods are studied, and existing datasets is analyzed. However, the existing solutions either lack full 3D detection and localization capabilities or are unsuitable for the use case at the airport aprons. This paper introduces a novel pipeline for real-time object detection and localization for airport aprons, by combining object detection and segmentation in 2D images, 2D and 3D sensors data fusion, object localization through 3D point cloud, and object orientation detection with temporal data. The apron is surveyed using a RGB camera and LiDAR sensor, to enable the automatic object detection and localization to provide real-time information about vehicles, personnel, and other objects present at the airport apron. The proposed approach integrates camera and LiDAR data for real-time object detection and localization, offering comprehensive scene awareness and enabling precise object detection and localization on airport aprons. The implemented technical solution is evaluated in terms of accuracy, localization precision, and time efficiency.