Efficient timing of aircraft docking and push‐back operations is crucial for enhancing the efficiency and reliability of civil aviation operations. Traditional methods suffer from significant data loss, high human involvement, and low accuracy, which are prone to inaccuracies that can disrupt airport scheduling and resource allocation. This paper introduces a reliable computer vision approach, YUAR (YOLOv7‐UAVMOT Aircraft Recognition), which leverages advanced detection algorithms and machine learning to improve accuracy and reduce human error in monitoring aircraft movements. Utilizing a newly developed Image and Video Dataset of Aircraft on Airport Surface (IV‐AAS), YUAR incorporates the YOLOv7‐based Aircraft Detection (YAD) algorithm with UAVMOT for dynamic tracking. This integration facilitates a multithreshold frame interpolation method, significantly enhancing the precision of tracking aircraft docking and push‐back events. Experiment results show that the system achieves a mean average precision (mAP) of 94.8% and an IDF1 score of 92.7%, demonstrating superior performance compared to existing methods such as YOLOv5 and DeepSORT by reducing identification switches. Additionally, the recognition rate of the docking and push‐back times under various operational scenarios reaches 100% with minute‐level precision. Our research offers significant implications for Airport Collaborative Decision Making (A‐CDM), optimizing the allocation of resources and improving the overall operational efficiency of airports.
Zhao et al. (Thu,) studied this question.