Marine vessels or ships have been considered one of the primary vehicles used for sea transportation, which can also be used as an intermediary tool to serve numerous other marine-related activities. In tracking and monitoring the activities of these ships, automatic vessel object detection is undoubtedly challenging to extract the number and position of the vessels from complex seawater backgrounds. In this study, we build a one-stage network of YOLOv5x6 based deep learning model on ShipRSImageNet large-scale dataset. With 50 ship categories, our model obtained a promising performance with a mean average precision of 75.18%. Our findings are potentially beneficial to support maritime security enforcement policy including counter-measuring illegal fisheries and managing seawater traffic surveillance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Marine Vessels Detection on Very High-Resolution Remote Sensing Optical Satellites using Object-Based Deep Learning




    Erscheinungsdatum :

    03.11.2022


    Format / Umfang :

    776410 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    TRENDS IN VERY HIGH RESOLUTION EARTH OBSERVATION USING OPTICAL SMALL SATELLITES

    Kim, Eugene D. / Moon, Yongjun / Lee, Hungu et al. | TIBKAT | 2020


    Remote Sensing Satellites

    Maini, Anil K. ;Agrawal, Varsha | Wiley | 2014


    China’s Remote Sensing Satellites

    Chandrashekar, S. | Springer Verlag | 2022


    REMOTE INSPECTION OF MARINE VESSELS

    SEAH ROBERT KM / JIN ZHAOHUI / SRIVASTAVA ANUP et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Remote Inspection of Marine Vessels

    SEAH ROBERT KM / JIN ZHAOHUI / SRIVASTAVA ANUP et al. | Europäisches Patentamt | 2025

    Freier Zugriff