Sparse convolutional model with semantic expression for waste electrical appliances recognition

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外文摘要：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.

外文关键词：

sparse convolutional modeldeep neural networksemantic expressionvisualizationcomputer vision

作者：

HAN HongGui、LIU YiMing、LI FangYu、DU YongPing

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作者单位：

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

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基金：

National Key Research and Development ProjectNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaBeijing Outstanding Young Scientist ProgramBeijing Natural Science FoundationBeijing Youth Scholar

项目编号：

2022YFB3305800-561903010621253016202100361890930-5BJJWZYJH01201910005020KZ202110005009037

出版年：

2024

DOI：

10.1007/s11431-023-2650-x

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

年,卷(期)：2024.67(9)