Aiming at the problems of target loss and poor real-time performance in ship detection at sea, a target detection method based on improved YOLOv4-Tiny network was proposed to realize real-time detection of ship video. Firstly, an output prediction scale of 52×52 is added to the network structure to improve the detection ability of small targets on ships. Secondly, based on the improved model structure and self-established data set, the priori anchor frame was redesigned. Finally, the mish activation function is used to improve the model performance of YOLOv4-Tiny. The experimental results show that the improved YOLOv4-Tiny algorithm not only retains the great advantage of fast detection speed, but also significantly improves the detection accuracy. It can realize real-time high-precision detection of ship video, which is of great significance for maritime safety law enforcement and supervision.


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    Titel :

    Ship video detection based on improved YOLOv4-Tiny


    Beteiligte:
    Kong, Liuling (Autor:in) / Liu, Xiuwen (Autor:in)

    Kongress:

    International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022) ; 2022 ; Luoyang,China


    Erschienen in:

    Proc. SPIE ; 12303


    Erscheinungsdatum :

    28.07.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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