Unmanned Aerial Vehicles (UAVs) are playing an important role in the development of smart maritime. However, images crowned with small-sized and highly dense cause the accuracy to decrease for ship detection under UAV vision. Aiming at the problem, this paper proposes an improved YOLOv5 to detect ships accurately under UAV vision and combines with deepsort to realize ship tracking. Firstly, we add a detection layer to make full use of shallow features with rich detail information in the part of feature fusion. Then, the coordinate attention is introduced in YOLOv5 to focus on more important feature information. The test results show that the accuracy, recall and average precision of the proposed SA-YOLOv5 are improved by 3.4%, 0.3% and 1.0% compared with YOLOv5. Finally, the deepsort is used as the tracker to realize the real-time ship tracking under UAV vision.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Maritime Vessel Detection and Tracking under UAV Vision


    Beteiligte:
    Li, YongShuai (Autor:in) / Yuan, Haiwen (Autor:in) / Wang, Yuan (Autor:in) / Zhang, Bulin (Autor:in)


    Erscheinungsdatum :

    16.09.2022


    Format / Umfang :

    428698 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-information Collaboration for Maritime Vessel Detection and Tracking

    Sun, Yue / Zhao, Chunqing / Zhao, Zhao et al. | Springer Verlag | 2025


    Aerial Maritime Vessel Detection and Identification

    Kulas, Antonella Barisic / Petric, Frano / Bogdan, Stjepan | IEEE | 2025



    Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction

    Perera, Lokukaluge P. / Oliveira, Paulo / Guedes Soares, C. | IEEE | 2012


    MARITIME VESSEL STABILIZER

    TEPPIG JR / WHANG JAMES S | Europäisches Patentamt | 2025

    Freier Zugriff