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作者:

Zhang, Jian (Zhang, Jian.) | Yu, Jianbo (Yu, Jianbo.) | Yu, Gang (Yu, Gang.) | Sun, Rongcheng (Sun, Rongcheng.) | Tang, Jian (Tang, Jian.)

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摘要:

The municipal solid waste incineration has great advantages for resource recovery and utilization, and the stability of its combustion state is of great significance to the control process. However, the problem of low accuracy still exists in the classification of the images of municipal solid waste incineration (MSWI). For this reason, this paper proposes an image classification algorithm for MSWI based on attention module. The framework of ResNet is employed to improve the depth and accuracy of the network, which solves the problem of insufficient depth of the traditional network while suffering from the deficiency of slow convergence speed. Consequently, the attention module is embedded to solve the problem of different importance of different parts in the incineration flame image. The results show that the algorithm improves the recognition accuracy of municipal solid waste incineration flame images to 96.7%, and the convergence speed is significantly improved compared to other basic neural network models. © 2024 IEEE.

关键词:

Municipal solid waste Neural network models Image enhancement Image classification Waste incineration

作者机构:

  • [ 1 ] [Zhang, Jian]Nanjing University Of Information Science & Technology, School Of Computer Science, Nanjing, China
  • [ 2 ] [Yu, Jianbo]Nanjing University Of Information Science & Technology, NUIST-TianChang Research Institute, Nanjing, China
  • [ 3 ] [Yu, Gang]State & Beijing Key Laboratory Of Process Automation In Mining & Metallurgy, Beijing, China
  • [ 4 ] [Sun, Rongcheng]Nanjing University Of Information Science & Technology, NUIST-TianChang Research Institute, Nanjing, China
  • [ 5 ] [Tang, Jian]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China

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年份: 2024

页码: 1725-1730

语种: 英文

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SCOPUS被引频次: 1

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