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Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2020-11-30 , DOI: 10.1155/2020/5795976
Jian-ye Yuan 1 , Xin-yuan Nan 1 , Cheng-rong Li 2 , Le-le Sun 3
Affiliation  

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

中文翻译:

基于卷积神经网络的实时多个单一垃圾分类研究

考虑到垃圾分类的迫切性,本文设计了一个23层的卷积神经网络(CNN)模型,重点是实时垃圾分类,解决了垃圾分类和回收的准确性低以及手工难度大的问题。回收。首先,使用深度可分离卷积来减少模型的参数。然后,利用注意力机制提高垃圾分类模型的准确性。最后,使用模型微调方法进一步提高了垃圾分类模型的性能。此外,我们将该模型与经典图像分类模型(包括AlexNet,VGG16和ResNet18)以及轻量分类模型(包括MobileNetV2和SuffleNetV2)进行了比较,发现模型GAF_dense的准确率更高,更少的参数和FLOP。为了进一步检查模型的性能,我们测试了CIFAR-10数据集,发现模型的准确率(GAF_dense)分别比ResNet18和SufflenetV2高0.018和0.03。在ImageNet数据集中,模型的准确率(GAF_dense)分别比Resnet18和SufflenetV2高0.225和0.146。因此,本文提出的垃圾分类模型适用于垃圾分类及其他分类任务,以保护生态环境,可应用于环境科学,儿童教育,环境保护等分类任务。03分别高于ResNet18和SufflenetV2。在ImageNet数据集中,模型的准确率(GAF_dense)分别比Resnet18和SufflenetV2高0.225和0.146。因此,本文提出的垃圾分类模型适用于垃圾分类及其他分类任务,以保护生态环境,可应用于环境科学,儿童教育,环境保护等分类任务。03分别高于ResNet18和SufflenetV2。在ImageNet数据集中,模型的准确率(GAF_dense)分别比Resnet18和SufflenetV2高0.225和0.146。因此,本文提出的垃圾分类模型适用于垃圾分类及其他分类任务,以保护生态环境,可应用于环境科学,儿童教育,环境保护等分类任务。
更新日期:2020-12-01
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