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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    Title :

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


    Contributors:
    Liang, Huimin (author) / Li, Qinlin (author) / Liao, Kefei (author) / Jiang, Junzheng (author) / Jing, Mojie (author)


    Publication date :

    2025-05-23


    Size :

    633029 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English