As airport facilities upgrade and the demand for air transportation increases, solving the problem of runway safety becomes particularly important. In order to ensure the safe takeoff and landing of aircraft, Foreign Object Debris (FOD) on the runway should be accurately and quickly detected during runway detection to reduce the probability of danger. At present, FOD detection of most airport runways still adopts the traditional manual inspection, which has low efficiency and high cost. An intelligent recognition system for vehicle-mounted FOD management based on YOLOv8 was proposed in this paper, applicable to real airport scenarios, and details the design of the system's core algorithm, emphasizing the YOLOv8 framework in the detection module. A new data enhancement algorithm is proposed to ensure the high performance of the system under complex environment changes. The experimental results show that the accuracy rate of the system is higher than 95%, and it demonstrates strong robustness in complex scenes.


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    Titel :

    Vehicle airport runway FOD assisted intelligent identification system


    Beteiligte:
    Wang, Zhe (Herausgeber:in) / Ding, Hongbin (Herausgeber:in) / Xie, Ruilin (Autor:in) / Ye, Demao (Autor:in) / Zhan, Chunlian (Autor:in)

    Kongress:

    Conference on Spectral Technology and Applications (CSTA 2024) ; 2024 ; Dalian, China


    Erschienen in:

    Proc. SPIE ; 13283


    Erscheinungsdatum :

    04.12.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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