A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning

Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, wh...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2022-06, Vol.2022, p.1-9
Main Authors: Yuan, Zhen, Liu, Jinfeng
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Cardboard
Classification
Datasets
Deep learning
Ecological effects
Environmental impact
Experiments
Garbage
Image classification
Machine learning
Medical research
Medical wastes
Methods
Neural networks
Recycling
Resource utilization
Support vector machines
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Summary:Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model.
ISSN:2090-0147
2090-0155
DOI:10.1155/2022/7608794