The waterway traffic is recently getting busier due to the strong development of the shipping industry. There are frequent collisions and other accidents between ships, it is necessary to detect these types of ships effectively to ensure waterway traffic safety. Ship detection technology based on computer vision employing optical remote sensing images has great significance to improve port management and maritime inspection. In recent years, convolutional neural networks (CNN) have achieved good results in ship target detection and recognition. In this paper, we train the YOLOv3 and the latest YOLOv4 model on the dataset. The experimental results show that YOLOv4 can be applied well in the field of ship detection and classification from optical remote sensing. Based on the obtained results, we compare the effectiveness of the models when applied to actual training on the same data set.


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    Titel :

    A Comparison of YOLO Networks for Ship Detection and Classification from Optical Remote-Sensing Images


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Abraham, Ajith (Herausgeber:in) / Hong, Tzung-Pei (Herausgeber:in) / Kotecha, Ketan (Herausgeber:in) / Ma, Kun (Herausgeber:in) / Manghirmalani Mishra, Pooja (Herausgeber:in) / Gandhi, Niketa (Herausgeber:in) / Trung, Ha Duyen (Autor:in)

    Kongress:

    International Conference on Hybrid Intelligent Systems ; 2022 December 13, 2022 - December 15, 2022



    Erscheinungsdatum :

    2023-05-25


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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