Risk assessment model for dust explosion in dust removal pipelines using an attention mechanism-based convolutional neural network

HIGHLIGHTS

• What: This study proposes an attention mechanism-based convolutional neural_network (ConvNext-Tsc) to estimate dust thickness from images and the explosion risk in dust removal pipes. To address this critical task, the authors propose a ConvNeXt-Tsc model based on an attention mechanism. The CBAM enables the model to focus on important features and perform feature selection across scales by fusing channel-wise and spatial attention mechanisms. This model has the advantages of the Swin Transformer and the structure of ConvNeXt, providing a solid foundation for accurate image recognition and classification.
• Who: Posted Date October . . .
  

   

  

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