Garbage image classification method based on improved convolution neural network and long short-term memory network
Pages 657 - 663
Abstract
When dealing with garbage image classification task, with the deepening of network layer, convolutional neural network will lead to gradient disappearance / explosion and large time consumption. Therefore, a garbage image classification method combining improved convolutional neural network and long short-term memory network is proposed. Taking ResNet-50 as the network backbone, it is optimized by using deep separable convolution and attention mechanism. At the same time, supplemented by LSTM, the features extracted by convolution network and cyclic network are fused to complete classification output. Ablation experiments are carried out on this model and compared with other typical convolutional neural networks. The results show that the accuracy of this model increases by an average of 4.5%. The introduction of deep separable convolution can reduce the training time by about 35.4% compared with the baseline method.
References
[1]
Bureau of statistics of the people's Republic of China. China Statistical Yearbook [M]. Beijing: China Statistics Press, 2020. (in Chinese).
[2]
Yue Xiaohua.Improve the local laws and regulations system of domestic waste classification [N]. China Environment News, 2021-08-16(003).(in Chinese).
[3]
KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks [C]//International Conference on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc, 2012: 1097-1105.
[4]
SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE,2015: 1-9.
[5]
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [EB/OL]. [2021-02-22]. http://arxiv.org/abs/1409.1556.
[6]
HE K, ZHANG X, REN S, Deep residual learning for image recognition [C]// 2016 the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016:770-778.
[7]
Kang Zhuang, Yang Jie, Guo Haoqi. Design of automatic garbage classification system based on machine vision [J]. Journal of Zhejiang University (Engineering Edition),2020, 54(7): 1272-1280. (in Chinese).
[8]
GAO Ming, CHEN Yuhan, ZHANG Zehui, Garbage image classification algorithm based on new spatial attention mechanism and transfer learning[J]. System Engineering Theory and Practice, 2021, 41(2): 498-512. (in Chinese).
[9]
Ying Liu,Zhishan Ge,Guoyun Lv,Shikai Wang. Research on Automatic Garbage Detection System Based on Deep Learning and Narrowband Internet of Things[J].Journal of Physics: Conference Series,2018,1069(1):012032.
[10]
Bircanoglu C, Atay M, Be ş er F, et al. Recyclenet: Intelligent waste sorting using deep neural networks[C]// 2018 Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2018: 1-7.
[11]
Mao W L, Chen W C, Wang C T, et al. Recycling waste classification using optimized convolutional neural network[J]. Resources, Conservation and Recycling, 2021, 164: 105132.
[12]
Adedeji O,Wang Z. Intelligent waste classification system using deep learning convolutional neural network[J]. ProcediaManufacturing, 2019, 35: 607-612.
[13]
Song F, Zhang Y, Zhang J. Optimization of CNN-based Garbage Classification Model[C]// Proceedings of the 4th International Conference on Computer Science and Application Engineering. 2020: 1-5.
[14]
Yuan Jianye, Nan Xinyuan, Cai Xin, Li Chengrong. Garbage image classification method by lightweight residual network[J].Environmental Engineering,2021,39(02):110-115.(in Chinese).
[15]
Pan lie, Zeng Cheng, Zhang Haifeng, Text emotion analysis method combining generalized autoregressive pre training language model and cyclic convolution neural network [J/OL]. Computer application:1-9[2021-09-15].http://kns.cnki.net/kcms/detail/51.1307.TP.20210906.1654.002.html.(in Chinese).
[16]
Fan Hao, Li Pengfei. Emotion analysis of short text based on FastText word vector and bidirectional GRU recurrent neural network -Taking microblog comment text as an example [J]. Information science, 2021,39(04):15-22.(in Chinese).
[17]
Li Jun, Li Ming. Research on facial expression recognition based on multi scale CNN and Bi- LSTM [J]. Journal of Beijing Union University,2021,35(01):35-39+44.(in Chinese).
[18]
Liang Lianhui, Li Jun, Zhang Shaoquan. Hyperspectral image classification method based on 3D octave convolution and Bi -RNN attention network [J/OL]. Acta PHOTONICA Sinica:1-13[2021-09-15].http://kns.cnki.net/kcms/detail/61.1235.O4.20210831.1701.058.html.(in Chinese).
[19]
Hochreiter S, Schmidhuber J. Long short-term memory.Neural Computation, 1997, 9(8): 1735–1780. [
[20]
WOO S, PARK J, LEE J Y, CBAM: convolutional block attention module [C]// European Conference on Computer Vision. Munich: Springer, 2018: 3–19.
[21]
CHOLLET, François. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017:1251-1258.
Index Terms
- Garbage image classification method based on improved convolution neural network and long short-term memory network
Index terms have been assigned to the content through auto-classification.
Recommendations
Aviation visibility forecasting by integrating Convolutional Neural Network and long short-term memory network
Weather forecasts are essential to aviation safety. Unreliable forecasts not only cause problems to pilots and air traffic controllers, but also lead to aviation accidents and incidents. To enhance the forecast accuracy, an integrated model comprising a ...
Long-Short-Term Memory Based on Adaptive Convolutional Network for Time Series Classification
AbstractDeep learning technology is effective to solve time series classification tasks. The existing deep learning algorithms with fixed step convolution cannot effectively extract and focus on multi-scale features. Based on the complexity and long-term ...
ResNext50 based convolution neural network-long short term memory model for plant disease classification
AbstractAgricultural problems need to be dealt with advanced computing methods to increase food productivity. Automatic classification of plant disease using deep learning methods helps to analyze the food quality and productivity. The existing methods ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2022
753 pages
ISBN:9781450397780
DOI:10.1145/3569966
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 December 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSSE 2022
CSSE 2022: 2022 5th International Conference on Computer Science and Software Engineering
October 21 - 23, 2022
Guilin, China
Acceptance Rates
Overall Acceptance Rate 33 of 74 submissions, 45%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 30Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format