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%.
A convolutional neural network-based method for marine ship waste classification
International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024) ; 2024 ; Yinchuan, China
Proc. SPIE ; 13259
2024-09-04
Conference paper
Electronic Resource
English
Ship classification based on convolutional neural networks
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