Sparse convolutional model with semantic expression for waste electrical appliances recognition

HAN HongGui ¹LIU YiMing ¹LI FangYu ¹DU YongPing²

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作者信息

1. Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China;Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China;Beijing Artificial Intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection,Beijing 100124,China
2. Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China
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Abstract

Deep neural networks play an important role in the recognition of waste electrical appliances.However,deep neural network components still lack reliability in decision-making features.To address this problem,a sparse convolutional model with semantic expression(SCMSE)is proposed.First,a low-rank sparse semantic expression component,combining the benefits of residual networks and sparse representation,is adapted to enhance sparse feature extraction and semantic expression.Second,a reliable network architecture is obtained by iterating the optimal sparse solution,enhancing semantic expression.Finally,the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

Key words

sparse convolutional model/deep neural network/semantic expression/visualization/computer vision

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基金项目

National Key Research and Development Project(2022YFB3305800-5)

National Natural Science Foundation of China(61903010)

National Natural Science Foundation of China(62125301)

National Natural Science Foundation of China(62021003)

National Natural Science Foundation of China(61890930-5)

Beijing Outstanding Young Scientist Program(BJJWZYJH01201910005020)

Beijing Natural Science Foundation(KZ202110005009)

Beijing Youth Scholar(037)

出版年

2024

中国科学:技术科学(英文版)

中国科学院

中国科学:技术科学(英文版)

CSTPCDEI

影响因子：1.056

ISSN：1674-7321

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