Aiming at the challenges of significant scale and perspective variations of targets, marine environmental variability, and dataset scarcity in ship detection under drone vision, this paper proposes the EPA-YOLOv10 ship detection model to effectively suppress wave interference and enhance small target detection accuracy. At the algorithmic level: (1) the C2f-EMSC multi-scale feature enhancement module is designed to achieve local and global feature fusion of ships and multi-scale integration of distant and nearby ship targets; (2)the PTSSA spatiotemporal attention mechanism is proposed to suppress sea surface interference through reverse spatial attention. Additionally, to address the scarcity of ship data, a multi-source ship dataset containing four major ship categories is constructed. Experiments show that the detection accuracy of the proposed model on the self-built dataset Myship is significantly improved with mAP0.5.


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    Titel :

    Anti-Interference and Multi-Scale Ship Detection Algorithm Based on YOLOv10


    Beteiligte:
    Liang, Huimin (Autor:in) / Li, Qinlin (Autor:in) / Liao, Kefei (Autor:in) / Jiang, Junzheng (Autor:in) / Jing, Mojie (Autor:in)


    Erscheinungsdatum :

    23.05.2025


    Format / Umfang :

    633029 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch