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%.


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    Title :

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


    Contributors:
    Na, Jing (editor) / He, Shuping (editor) / Zhang, Le (author) / Zhang, Jie (author) / Tan, Yanghui (author)

    Conference:

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


    Published in:

    Proc. SPIE ; 13259


    Publication date :

    2024-09-04





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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