Open AccessX-DenseNet: Deep Learning for Garbage Classification Based on Visual Images
Author(s) -
Sha Meng,
Ning Zhang,
Yunwen Ren
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1575/1/012139
Subject(s) - garbage , computer science , preprocessor , convolutional neural network , artificial intelligence , data pre processing , feature (linguistics) , deep learning , process (computing) , artificial neural network , pattern recognition (psychology) , data mining , machine learning , philosophy , linguistics , programming language , operating system
In order to effectively solve the problem of garbage classification, this paper designs a garbage classification model based on deep convolutional neural network. Based on Xception network, combined with the idea of dense connections and multi-scale feature fusion in DenseNet, the X-DenseNet is constructed to classify the garbage images obtained by visual sensors. This paper conducts experiments through the process of “obtaining dataset-preprocessing data-building X-DenseNet model-training and testing model” and the accuracy of the model on the testing set is up to 94.1%, which exceeds some classic classification networks. The X-DenseNet automatic garbage classification model based on visual images proposed in this paper can effectively reduce manual investment and improve the garbage recovery rate. It has the vital scientific significance and application value.