Foreign Object Debris (FOD) is the second most significant aviation safety hazard after bird strikes. Existing FOD detection equipment, situated at a considerable distance from the runway, often results in poor imaging quality for small targets, which impacts detection performance and is also costly. Therefore, this study has developed a highly mobile and low-cost multi-functional runway operation robot that can conduct rapid automatic inspections during runway idle periods. By equipping the robot with high-resolution cameras and computing devices, the detection accuracy and real-time performance of FOD can be significantly improved. To further enhance the detection effectiveness of FOD, this study also proposes a FOD data augmentation method and an improved network structure based on YOLO. Experimental results demonstrate that the refined model has achieved a notable improvement of $\mathbf{2 . 1 \%}$ in precision compared to the traditional YOLOv5 model, with a frame rate increase of approximately 45 frames.


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    Titel :

    Runway Multi-functional Robot and Small Targets Detecting Method


    Beteiligte:
    Yan, Kai (Autor:in) / Wei, Kaizhen (Autor:in) / Liu, Weiqian (Autor:in) / Che, Yaoming (Autor:in) / Shu, Yuanjun (Autor:in)


    Erscheinungsdatum :

    23.10.2024


    Format / Umfang :

    922786 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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