Recyclable waste image recognition based on deep learning

doi:10.1016/j.resconrec.2021.105636

	Recyclable waste image recognition based on deep learning
	Zhang, Qiang1 ; Zhang, Xujuan 1; Mu, Xiaojun2 ; Wang, Zhihe1 ; Tian, Ran1 ; Wang, Xiangwen1 ; Liu, Xueyan3
	2021-08
在线发表时间	2021-05
发表期刊	Resources, Conservation and Recycling
ISSN	0921-3449
卷号	171
摘要	This study aims to improve the accuracy of waste sorting through deep learning and to provide a possibility for intelligent waste classification based on computer vision/mobile phone terminals. A classification model of recyclable waste images based on deep learning is proposed in this paper. In this waste classification model, the self-monitoring module is added to the residual network model, which can integrate the relevant features of all channel graphs, compress the spatial dimension features, and have a global receptive field. But the number of channels is still kept unchanged; thereby, the model can improve the representation ability of the feature map and can automatically extract the features of different types of waste images. The proposed model was tested on the TrashNet dataset to classify recyclable waste and compare its classification performance with other algorithms. Experimental results show that the image classification accuracy of this model reaches 95.87%. © 2021
关键词	Classification (of information) Deep learning Image classification Image enhancement Classification models Deep learning Image-based Recyclable waste classification Recyclable wastes Residual network Self-monitoring Self-monitoring module Waste classification Waste sorting
DOI	10.1016/j.resconrec.2021.105636
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Engineering ; Environmental Sciences & Ecology
WOS类目	Engineering, Environmental ; Environmental Sciences
WOS记录号	WOS:000667310900021
出版者	Elsevier B.V.
EI入藏号	20211810299770
EI主题词	Image recognition
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 903.1 Information Sources and Analysis
原始文献类型	Journal article (JA)
引用统计	被引频次：55[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.nwnu.edu.cn/handle/39RV6HYL/72689
专题	机关部门（群众团体）_教务处实体学院_数学与统计学院实体学院_数学与统计学院_数学系实体学院_计算机科学与工程学院机关部门（群众团体）_党委学生工作部（学生社区党工委、大学生艺术教育中心）直属单位_创新创业学院实体学院_敦煌学院
通讯作者	Liu, Xueyan
作者单位	1.Northwest Normal Univ, Dept Comp Sci & Engn, Lanzhou 730070, Gansu, Peoples R China; 2.Northwest Normal Univ, Dept Innovat & Entrepreneurship, Lanzhou 730070, Gansu, Peoples R China; 3.Northwest Normal Univ, Dept Math & Stat, Lanzhou 730070, Gansu, Peoples R China
第一作者单位	计算机科学与工程学院
通讯作者单位	数学与统计学院
第一作者的第一单位	计算机科学与工程学院
推荐引用方式 GB/T 7714	Zhang, Qiang,Zhang, Xujuan,Mu, Xiaojun,et al. Recyclable waste image recognition based on deep learning[J]. Resources, Conservation and Recycling,2021,171.
APA	Zhang, Qiang.,Zhang, Xujuan.,Mu, Xiaojun.,Wang, Zhihe.,Tian, Ran.,...&Liu, Xueyan.(2021).Recyclable waste image recognition based on deep learning.Resources, Conservation and Recycling,171.
MLA	Zhang, Qiang,et al."Recyclable waste image recognition based on deep learning".Resources, Conservation and Recycling 171(2021).