With the rapid development of UAV technology and aerial surveillance systems, efficient and accurate aircraft inspection techniques have become increasingly important. In this paper, we propose an improved YOLOv8-based model for improving the accuracy and efficiency of aircraft detection. We integrate the Double Attention mechanism to capture the key features of the aircraft more efficiently; employ the Adaptive Feature Pyramid Network (AFPN) for multi-scale feature processing to improve the detection capability for aircraft of different sizes; and introduce the Multi-Path Decoupled Intersection over Union (MPDIOU) to optimize the accuracy of the bounding box. Through a series of experiments on a standard aircraft inspection dataset, our model can achieve an average precision mean of 96.8% in real-time aircraft inspection, while the recall rate can reach 95%. Our model demonstrates a significant improvement in various key performance metrics, which proves the potential and effectiveness of the proposed method in practical applications.


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    Titel :

    Aerospace Aircraft Inspection Based on Improved YoloV8


    Beteiligte:
    Chen, Yonglu (Autor:in) / Zhang, Rui (Autor:in) / Zhang, Beibei (Autor:in)


    Erscheinungsdatum :

    17.11.2023


    Format / Umfang :

    862223 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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