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<p>The increasing production of disposable plastic products contributes greatly to marine pollution and its impact on the marine ecosystem and organisms consuming ocean-derived food. To address this issue, this paper proposes a new customized convolutional neural network (CNN) model for categorizing the level of marine pollution in underwater ocean regions using image classification. The customized CNN model is developed and compared with five preexisting models, including DenseNet121, Inception-ResNetV2, InceptionV3, VGG-19, and VGG-16. The results show that the customized model achieves an accuracy of 99.5% and performs optimally according to various performance metrics. The model is implemented on an edge AI device, such as Raspberry Pi, to bring it to practical use.</p>

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Edge AI-powered marine pollution classification with customized CNN model

  • 期刊论文

  • 发表日期：

    2025年1月14日

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  • Sanjai Palanisamy,
  • Talal Bonny,
  • Nida Nasir,
  • Mohammad Al Shabi,
  • Ahmed Al Shammaa

摘要

The increasing production of disposable plastic products contributes greatly to marine pollution and its impact on the marine ecosystem and organisms consuming ocean-derived food. To address this issue, this paper proposes a new customized convolutional neural network (CNN) model for categorizing the level of marine pollution in underwater ocean regions using image classification. The customized CNN model is developed and compared with five preexisting models, including DenseNet121, Inception-ResNetV2, InceptionV3, VGG-19, and VGG-16. The results show that the customized model achieves an accuracy of 99.5% and performs optimally according to various performance metrics. The model is implemented on an edge AI device, such as Raspberry Pi, to bring it to practical use.