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

Le Zhang; Jie Zhang; Yanghui Tan

doi:10.1117/12.3039399

4 September 2024 A convolutional neural network-based method for marine ship waste classification

Le Zhang, Jie Zhang, Yanghui Tan

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132591Q (2024) https://doi.org/10.1117/12.3039399
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

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%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Le Zhang, Jie Zhang, and Yanghui Tan "A convolutional neural network-based method for marine ship waste classification", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132591Q (4 September 2024); https://doi.org/10.1117/12.3039399

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