The increasing amount of solid waste is becoming a significant problem that needs to be addressed urgently. The reliable and accurate classification method is a crucial step in waste disposal because different types of wastes have different disposal ways. The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Their performance on different datasets is varied, and there is also a lack of specific large‐scale datasets for training. We propose a new combination classification model based on three pretrained CNN models (VGG19, DenseNet169, and NASNetLarge) for processing the ImageNet database and achieve high classification accuracy. In our proposed model, the transfer learning model based on each pretrained model is constructed as a candidate classifier, and the optimal output of three candidate classifiers is selected as the final classification result. The experiments based on two waste image datasets demonstrate that the proposed model achieves 96.5% and 94% classification accuracy and outperforms several counterpart methods.

中文翻译：

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基于迁移学习的垃圾分类组合模型

不断增加的固体废物数量正成为一个亟待解决的重大问题。可靠和准确的分类方法是废物处理的关键步骤，因为不同类型的废物有不同的处理方式。现有的深度学习驱动的垃圾分类模型不容易达到准确的结果，由于采用了各种架构网络，仍需要改进。它们在不同数据集上的表现各不相同，也缺乏特定的大规模数据集用于训练。我们基于三个预训练的 CNN 模型（VGG19、DenseNet169 和 NASNetLarge）提出了一种新的组合分类模型，用于处理 ImageNet 数据库并实现高分类精度。在我们提出的模型中，构建基于每个预训练模型的迁移学习模型作为候选分类器，选择三个候选分类器的最优输出作为最终分类结果。基于两个废弃图像数据集的实验表明，所提出的模型达到了 96.5% 和 94% 的分类准确率，并且优于几种对应的方法。