Automatic garbage classification system based on machine vision

被引：2

作者：

Kang Z. ^{[1
]}

Yang J. ^{[1
]}

Guo H.-Q. ^{[1
]}

机构：

[1] School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2020年 / 54卷 / 07期

关键词：

Artificial intelligence; Image classification; Inception v3; Intelligent trash bin; Machine vision; Transfer learning;

D O I：

10.3785/j.issn.1008-973X.2020.07.004

中图分类号：

学科分类号：

摘要：

An automatic garbage classification system was designed based on machine vision in order to improve the efficiency of front-end collection in garbage classification process. The hardware device of the garbage classification system was designed and manufactured, which mainly included two boxes, the recyclable box and the non-recyclable box. A method of garbage type recognition was proposed based on Inception v3 feature extraction network structure and migration learning aiming at the data lacking problem caused by small garbage data sets. The method was trained and tested on the constructed garbage data set. The test results show that the method can accurately identify garbage types with an average accuracy of 0.99. The trained model was deployed on the raspberry pi 3B+, and tested on the real garbage bin. When the whole system was running stably, the average time for the system to complete the classification of one garbage was 0.95 second. The experimental results show that the automatic garbage classification system can effectively identify the types of garbage and complete the classification and recycling of the garbage. © 2020, Zhejiang University Press. All right reserved.

引用

页码：1272 / 1280and1307

共 25 条

[1]

WANG Jia-jia, ZHAO Na-na, LI Jin-hui, Present status and prevention of marine microplastics pollution in China, China Environmental Science, 39, 7, pp. 3056-3063, (2019)

[2]

LI Wen-han, MA Zeng-yi, YANG En-quan, Et al., Study on characteristics of electric dust removal fly ash and bag fly ash in circulating fluidized bed waste incineration system, Chinese Journal of Electrical Engineering, 39, 5, pp. 1397-1405, (2019)

[3]

REN Yue, YANG Jun-jie, CAO Hong-jun, Et al., All components resourcing system of rural garbage based on post-gather automatic sorting and disposal technology and its application, Transactions of the Chinese Society of Agricultural Engineering, 35, 4, pp. 248-254, (2019)

[4]

ZHAO Dong-e, WU Rui, ZHAO Bao-guo, Et al., Research on garbage classification and recognition of hyperspectral imaging, Spectroscopy and Spectral Analysis, 39, 3, pp. 921-926, (2019)

[5]

PAN Peng, HU Yun-feng, QIN Jin-zhi, Et al., A smart trash can based on NB-IOT, Science and Technology, 1, (2019)

[6]

CHEN Jing-yi, LI Chun-yan, SHENG Yu-biao, Et al., Intelligent hardware design of intelligent trash can based on GPRS communication, Electric Engineering, 2, pp. 18-20, (2019)

[7]

BAI Gui-feng, Design of household intelligent wastebin based on STC89C52, Automation and Instrumentation, 34, 3, pp. 97-100, (2019)

[8]

SALIMI I, DEWANTARA B S B, WIBOWO I K., Visual-based trash detection and classification system for smart trash bin robot, 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, pp. 378-383, (2018)

[9]

SUDHA S, VIDHYALAKSHMI M, PAVITHRA K, Et al., An automatic classification method for environment: friendly waste segregation using deep learning, 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development, pp. 65-70, (2016)

[10]

SETIAWAN W, WAHYUDIN A, WIDIANTO G R., The use of scale invariant feature transform (SIFT) algorithms to identification garbage images based on product label, 2017 3rd International Conference on Science in Information Technology, pp. 336-341, (2017)

← 1 2 3 →