School of Mechatronic Engineering, Harbin Vocational & Technical College, Harbin, Heilongjiang, People's Republic of China.
PLoS One. 2023 Mar 16;18(3):e0282336. doi: 10.1371/journal.pone.0282336. eCollection 2023.
Today, the topic of waste separation has been raised for a long time, and some waste separation devices have been installed in large communities. However, the vast majority of domestic waste is still not properly sorted and put out, and the disposal of domestic waste still relies mostly on manual classification. The research in this paper applies deep learning to this persistent problem, which has important significance and impact. The domestic waste is classified into four categories: recyclable waste, kitchen waste, hazardous waste and other waste. The garbage classification model trained based on MobileNetV2 deep neural network can classify domestic garbage quickly and accurately, which can save a lot of labor, material and time costs. The absolute accuracy of the trained network model is 82.92%. In comparison with CNN network model, the classification accuracy of MobileNetV2 model is 15.42% higher than that of CNN model. In addition, the trained model is light enough to be better applied to mobile.
如今，垃圾分类的话题已经被提出了很长一段时间，并且在一些大型社区中已经安装了一些垃圾分类装置。然而，绝大多数生活垃圾仍然没有得到妥善分类和投放，生活垃圾的处理仍然主要依靠人工分类。本文的研究将深度学习应用于这个长期存在的问题，具有重要的意义和影响。将垃圾分为可回收垃圾、厨余垃圾、有害垃圾和其他垃圾四类。基于 MobileNetV2 深度神经网络训练的垃圾分类模型可以快速、准确地对生活垃圾进行分类，这可以节省大量的人力、物力和时间成本。训练后的网络模型的绝对准确率为 82.92%。与 CNN 网络模型相比，MobileNetV2 模型的分类准确率比 CNN 模型高出 15.42%。此外，训练后的模型足够轻量级，可以更好地应用于移动设备。
