This study is based on Convolutional Neural Networks for intelligent classification of ship waste, efficiently categorizing ship waste into nine classes: A-plastic wastes, B-food wastes, C-domestic wastes, D-cooking oil, E-incinerator ashes, F-operating wastes, G-animal carcasses, H-fishing gear, I-electronic waste. Building AlexNet, VGG16, ResNet-50, and Inception-V3 Convolutional Neural Network models on the MATLAB platform and assess them on a custom dataset. The results indicate that Inception-V3 performed the best with a classification accuracy of 95.1%. Additionally, this study utilizes a Weighted Cross-Entropy Loss function to optimize the model, suppressing distortion caused by imbalanced training sets, resulting in an overall accuracy of 95.3%.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A convolutional neural network-based method for marine ship waste classification


    Beteiligte:
    Na, Jing (Herausgeber:in) / He, Shuping (Herausgeber:in) / Zhang, Le (Autor:in) / Zhang, Jie (Autor:in) / Tan, Yanghui (Autor:in)

    Kongress:

    International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024) ; 2024 ; Yinchuan, China


    Erschienen in:

    Proc. SPIE ; 13259


    Erscheinungsdatum :

    04.09.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Ship classification based on convolutional neural networks

    Yang, Yang / Ding, Kaifa / Chen, Zhuang | Taylor & Francis Verlag | 2022


    Marine multi-ship encounter graph structured learning method based on graph convolutional neural network

    GAO MIAO / ZHANG ANMIN / HAN YU et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    MARINE WASTE TREATMENT SHIP

    KYUNG KUK HYUN / CHOI DONGMI | Europäisches Patentamt | 2022

    Freier Zugriff

    System and method for estimating ship performance coefficient using convolutional neural network

    KIM YOO CHUL / KIM MYOUNG SOO / HWANG SEUNG HYUN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Multi-label convolutional neural network based pedestrian attribute classification

    Zhu, Jianqing / Liao, Shengcai / Lei, Zhen et al. | British Library Online Contents | 2017