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Tsinghua Science and Technology 2022, 27(5): 793-803 https://doi.org/10.26599/TST.2021.9010072
Open Access | Issue | Published: 17 March 2022

New Benchmark for Household Garbage Image Recognition

Show Author's Information Zhize Wu,  Huanyi Li( ),  Xiaofeng Wang( ),  Zijun Wu,  Le Zou,  Lixiang Xu,  Ming Tan
School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China
School of Energy Materials and Chemical Engineering, Hefei University, Hefei 230601, China
Keywords:
image classification, benchmark, household garbage, deep convolutional neural networks
Cite this article:
Wu Z, Li H, Wang X, et al. New Benchmark for Household Garbage Image Recognition. Tsinghua Science and Technology, 2022, 27(5): 793-803. https://doi.org/10.26599/TST.2021.9010072
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Abstract Full text About this article

Household garbage images are usually faced with complex backgrounds, variable illuminations, diverse angles, and changeable shapes, which bring a great difficulty in garbage image classification. Due to the ability to discover problem-specific features, deep learning and especially convolutional neural networks (CNNs) have been successfully and widely used for image representation learning. However, available and stable household garbage datasets are insufficient, which seriously limits the development of research and application. Besides, the state-of-the-art in the field of garbage image classification is not entirely clear. To solve this problem, in this study, we built a new open benchmark dataset for household garbage image classification by simulating different lightings, backgrounds, angles, and shapes. This dataset is named 30 classes of household garbage images (HGI-30), which contains 18 000 images of 30 household garbage classes. The publicly available HGI-30 dataset allows researchers to develop accurate and robust methods for household garbage recognition. We also conducted experiments and performance analyses of the state-of-the-art deep CNN methods on HGI-30, which serves as baseline results on this benchmark.


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About this article

New Benchmark for Household Garbage Image Recognition

Show Author's information Zhize Wu, Huanyi Li( ), Xiaofeng Wang( ), Zijun Wu, Le Zou, Lixiang Xu, Ming Tan
School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China
School of Energy Materials and Chemical Engineering, Hefei University, Hefei 230601, China

Abstract

Household garbage images are usually faced with complex backgrounds, variable illuminations, diverse angles, and changeable shapes, which bring a great difficulty in garbage image classification. Due to the ability to discover problem-specific features, deep learning and especially convolutional neural networks (CNNs) have been successfully and widely used for image representation learning. However, available and stable household garbage datasets are insufficient, which seriously limits the development of research and application. Besides, the state-of-the-art in the field of garbage image classification is not entirely clear. To solve this problem, in this study, we built a new open benchmark dataset for household garbage image classification by simulating different lightings, backgrounds, angles, and shapes. This dataset is named 30 classes of household garbage images (HGI-30), which contains 18 000 images of 30 household garbage classes. The publicly available HGI-30 dataset allows researchers to develop accurate and robust methods for household garbage recognition. We also conducted experiments and performance analyses of the state-of-the-art deep CNN methods on HGI-30, which serves as baseline results on this benchmark.

Keywords: image classification, benchmark, household garbage, deep convolutional neural networks

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Publication history
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Publication history

Received: 07 June 2021
Revised: 20 August 2021
Accepted: 18 September 2021
Published: 17 March 2022
Issue date: October 2022

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© The author(s) 2022.

Acknowledgements

This paper was supported by the National Natural Science Foundation of China (Nos. 12001523, 11971046, 12131003, and 11871081), the Scientific Research Project of Beijing Municipal Education Commission (No. KM201910005012), and Beijing Natural Science Foundation Project (No. Z200002).

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