Ship target detection technology plays a vital role in civilian maritime traffic monitoring and military maritime security protection, and it is the key to ensuring the safety and order of marine activities. In a complex ocean environment, the background information of optical images is complex. The ship images are missed and wrongly detected due to the change in UAV shooting height, so a CSCGhost target detection algorithm is proposed. Experiments denote that compared with the traditional YOLOv10, the mAP increases by 3.2%, the accuracy increases by 9.1 %, and the recall increases by 3.2%, which has a good detection effect and is especially suitable for offshore operations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Optimization of Ship Small Target Detection Based on YOLOv10 in Complex Ocean Environment


    Beteiligte:
    Xu, Xichen (Autor:in) / Song, Yucun (Autor:in) / Ge, Quanbo (Autor:in) / Huang, Yanjun (Autor:in)


    Erscheinungsdatum :

    18.10.2024


    Format / Umfang :

    1770092 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    BRA-YOLOv10: UAV Small Target Detection Based on YOLOv10

    Quanyu Zhang / Xin Wang / Heng Shi et al. | DOAJ | 2025

    Freier Zugriff


    Car Object Detection: Comparative Analysis of YOLOv9 and YOLOv10 Models

    Ardac, Fatma Betul Kara / Erdogmus, Pakize | IEEE | 2024


    HawkEye Conv-Driven YOLOv10 with Advanced Feature Pyramid Networks for Small Object Detection in UAV Imagery

    Yihang Li / Wenzhong Yang / Liejun Wang et al. | DOAJ | 2024

    Freier Zugriff

    Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10

    Bakirci, Murat / Dmytrovych, Petro / Bayraktar, Irem et al. | IEEE | 2024