Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network
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. First...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/5795976 |
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doaj-d40c8672de27492fba02cbb2364065a62020-12-14T09:46:38ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/57959765795976Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural NetworkJian-ye Yuan0Xin-yuan Nan1Cheng-rong Li2Le-le Sun3School of Electrical Engineering, Xinjiang University, Urumqi 830047, ChinaSchool of Electrical Engineering, Xinjiang University, Urumqi 830047, ChinaIntelligent Manufacturing Technology and System Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaSchool of Electrical Engineering, Shandong Huayu University of Technology, Dezhou 253034, ChinaConsidering 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.http://dx.doi.org/10.1155/2020/5795976 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jian-ye Yuan Xin-yuan Nan Cheng-rong Li Le-le Sun |
spellingShingle |
Jian-ye Yuan Xin-yuan Nan Cheng-rong Li Le-le Sun Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network Mathematical Problems in Engineering |
author_facet |
Jian-ye Yuan Xin-yuan Nan Cheng-rong Li Le-le Sun |
author_sort |
Jian-ye Yuan |
title |
Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network |
title_short |
Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network |
title_full |
Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network |
title_fullStr |
Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network |
title_full_unstemmed |
Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network |
title_sort |
research on real-time multiple single garbage classification based on convolutional neural network |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/5795976 |
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