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Deep learning based recyclable waste classification

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Zhiming CaiAuthors Info & Claims

ICCIR '22: Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics

Pages 186 - 190

https://doi.org/10.1145/3548608.3559190

Published: 14 October 2022 Publication History

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Abstract

Waste classification has attracted more and more attention in recent years, which is an important part of building an eco-friendly city. Traditional manual garbage classification has poor efficiency and accuracy. In this paper, based on deep learning, the garbage classification algorithm I-ResNet50 is proposed to improve the ResNet50 network, and the geometric transformation of the original data is performed. The test set results show that the I-ResNet50 algorithm can achieve a classification accuracy of 62.6%, which is a substantial improvement in accuracy compared with the original method.

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Deep learning based recyclable waste classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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ICCIR '22: Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics

June 2022

905 pages

ISBN:9781450397179

DOI:10.1145/3548608

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]

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Association for Computing Machinery

New York, NY, United States

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Published: 14 October 2022

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ICCIR 2022

ICCIR 2022: 2022 2nd International Conference on Control and Intelligent Robot

June 24 - 26, 2022

Nanjing, China

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Overall Acceptance Rate 131 of 239 submissions, 55%

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An Ensemble Model for 2D-data Classification based on Classical & Deep Learning Classifier

Deep learning–based network application classification for SDN

Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification

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