Abstract
The flight and departure time nodes for the port and departure flights yield important information about the cooperative decision system of an airport. However, at present, because it would affect normal flight management, airports cannot obtain these data by technical means. By installing a camera on the airport apron and employing a regional convolutional neural network model to identify the targets in the video, such as the aircraft, staff, and working vehicle, the times of the milestone events were determined according to the identified changes in the target shape and target motion state. Furthermore, prior knowledge on the plane gliding curve and ground support operations was obtained by implementing the least squares method to fit the plane gliding curve, and subsequently used to compensate for the occlusion-induced recognition error and enhance the robustness of the algorithm. It was experimentally verified that the proposed target detection-based milestone event time recognition method is able to identify the flight times during the over-station, plane entry, and the milestone launch event.
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Lu, Z., Ji, T. (2020). A Target Detection-Based Milestone Event Time Identification Method. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_21
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DOI: https://doi.org/10.1007/978-3-030-04946-1_21
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