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• W4283165089 endingPage "105032" @default.
• W4283165089 startingPage "105032" @default.
• W4283165089 abstract "As it is widely known, several ground services are provided by the airports for the domestic and international flights of the commercial passenger aircraft. Some of these services are conducted during the period called as the turnaround which starts with the parking of the aircraft in the aprons before the flight and ends with their leave from the aprons for the flight. Turnaround processes achieved in short time periods allow using the limited airport resources including the service vehicles and staff effectively. In addition, commercial reputation losses and financial losses that may arise from delays can be reduced as well as the delay-associated turnaround penalties. In this article, a deep learning and computer vision based system that detects and allows monitoring the airport service actions is proposed. The proposed system is capable of analyzing all the primary ground services for an aircraft parking on its apron by employing the RGB video frame sequences obtained from a single fixed camera focusing on the apron. In the service detection and analysis modules of the proposed airport ground service analysis system, some deep learning-based subsystems and in-house-developed algorithms were included and utilized. For the training of the machine learning models, a study-specific dataset was used and the constructed learning models were evaluated on real-life cases. Experimental results obtained as a result of the performance evaluations show that the proposed system is quite successful with precision rates over 90% in the detection and analysis of the airport ground services. This study is one of the limited research studies in which deep learning and computer vision techniques have been applied to detect and analyze the ground service actions. The proposed system is also capable of real-time data processing/analysis and concurrent service action monitoring. Furthermore, it allows monitoring when the service is received by stamping the times of service start/end. In a consideration of industrial relevance or operational perspective, such a system may facilitate the airport ground service management noticeably and reduce the delay-associated costs caused by the timing of the ground services." @default.
• W4283165089 created "2022-06-21" @default.
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• W4283165089 date "2022-09-01" @default.
• W4283165089 modified "2023-10-09" @default.
• W4283165089 title "A turnaround control system to automatically detect and monitor the time stamps of ground service actions in airports: A deep learning and computer vision based approach" @default.
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• W4283165089 doi "https://doi.org/10.1016/j.engappai.2022.105032" @default.
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