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Original Research | Open Access

Metallic product recognition with dual attention and multi-branch residual blocks-based convolutional neural networks

Honggui Hana,b,c,d( ), Qiyu Zhanga,b, Fangyu Lia,b,c,d, Yongping Dua,b, Yifan Gue,f, Yufeng Wue,f
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
Engineering Research Center of Digital Community Ministry of Education, Beijing University of Technology, Beijing 100124, China
Beijing Artificial Intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection, Beijing 100124, China
College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
Institute of Circular Economy, Beijing University of Technology, Beijing 100021, China
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Abstract

Visual recognition technologies based on deep learning have been gradually playing an important role in various resource recovery fields. However, in the field of metal resource recycling, there is still a lack of intelligent and accurate recognition of metallic products, which seriously hinders the operation of the metal resource recycling industry chain. In this article, a convolutional neural network with dual attention mechanism and multi-branch residual blocks is proposed to realize the recognition of metallic products with a high accuracy. First, a channel-spatial dual attention mechanism is introduced to enhance the model sensitivity on key features. The model can focus on key features even when extracting features of metallic products with too much confusing information. Second, a deep convolutional network with multi-branch residual blocks as the backbone while embedding a dual-attention mechanism module is designed to satisfy deeper and more effective feature extraction for metallic products with complex characteristic features. To evaluate the proposed model, a waste electrical and electronic equipment (WEEE) dataset containing 9266 images in 18 categories and a waste household metal appliance (WHMA) dataset containing 11,757 images in 23 categories are built. The experimental results show that the accuracy reaches 94.31% and 95.88% in WEEE and WHMA, respectively, achieving high accuracy and high quality recycling.

Keywords

Deep convolutional neural networksMetallic product recognitionAttention mechanismMulti-branchMetal resource recycling

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Circular Economy
Volume 1 Issue 2,
December 2022
Article number: 100014
DOI: 10.1016/j.cec.2022.100014
Cite this article:
Han H, Zhang Q, Li F, et al. Metallic product recognition with dual attention and multi-branch residual blocks-based convolutional neural networks. Circular Economy, 2022, 1(2): 100014. https://doi.org/10.1016/j.cec.2022.100014

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Received: 29 June 2022
Revised: 18 August 2022
Accepted: 22 August 2022
Published: 07 October 2022
© 2022 The Author(s). Published by Elsevier B.V. on behalf of Tsinghua University Press.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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