A Study of Garbage Classification with Convolutional Neural Networks

Shanshan Meng, Wei Ta Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recycling is already a significant work for all countries. Among the work needed for recycling, garbage classification is the most fundamental step to enable cost-efficient recycling. In this paper, we attempt to identify single garbage object in images and classify it into one of the recycling categories. We study several approaches and provide comprehensive evaluation. The models we used include support vector machines (SVM) with HOG features, simple convolutional neural network (CNN), and CNN with residual blocks. According to the evaluation results, we conclude that simple CNN networks with or without residual blocks show promising performances. Thanks to deep learning techniques, the garbage classification problem for the target database can be effectively solved.

Original languageEnglish
Title of host publicationIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages152-157
Number of pages6
ISBN (Electronic)9781728149998
DOIs
Publication statusPublished - 2020 Feb
EventIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020 - Rajpura, Punjab, India
Duration: 2020 Feb 72020 Feb 15

Publication series

NameIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020 - Proceedings

Conference

ConferenceIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020
CountryIndia
CityRajpura, Punjab
Period20-02-0720-02-15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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