In aircraft maintenance, automated detection of aircraft surface defects is critical to ensure flight safety. In this paper, an aircraft surface defect detection method based on improved YOLOv8 is proposed. In order to enhance the feature extraction capability and global context awareness of the model, this paper introduces the CoTAttention module on the basis of YOLOv8, and adopts the SPD-Conv convolution operation for replacing the C 2 F convolutional layer. The experimental results show that the improved YOLOv8 outperforms the baseline model in terms of detection accuracy and recall for three types of defects, namely Crack, Dent and Rust. These improvements validate the effectiveness of the proposed method in complex defect detection tasks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Aircraft Surface Defect Detection Based on Improved YOLOv8


    Beteiligte:
    Ren, Qi (Autor:in) / Wang, Dan (Autor:in)


    Erscheinungsdatum :

    20.09.2024


    Format / Umfang :

    1940817 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Aerospace Aircraft Inspection Based on Improved YoloV8

    Chen, Yonglu / Zhang, Rui / Zhang, Beibei | IEEE | 2023


    Rail Fastener Defect Detection of Heavy Haul Railway Based on Improved YOLOv8

    Li, Xinman / Cao, Yuan / Wang, Feng et al. | IEEE | 2024


    EDGS-YOLOv8: An Improved YOLOv8 Lightweight UAV Detection Model

    Min Huang / Wenkai Mi / Yuming Wang | DOAJ | 2024

    Freier Zugriff

    Ship Detection Based on Improved YOLOv8 Algorithm

    Cao, Xintong / Shen, Jiayu / Wang, Tao et al. | IEEE | 2024


    Road disease detection algorithm based on improved YOLOv8

    Nitiavintokana, Francia / Guo, Shibo / Yuan, Pengfei et al. | SPIE | 2025