M³RTNet：Combustion State Recognition Model of MSWI Process Based on Res-Transformer and Three Feature Enhancement Strategies

Aim: ing at the problem of low accuracy of flame combustion state recognition in incinerator during municipal solid waste incineration, this paper proposes a Res-Transformer flame combustion state recognition model based on three feature enhancement strategies. In this paper, Res-Transformer is used as the backbone network of the model to effectively integrate local flame combustion features and global features. Firstly, we introduce an efficient multi-scale attention module into Resnet, which uses a multi-scale parallel sub-network to establish long and short dependencies; Then, a deformable multi-head attention module is designed in the Transformer layer, and the deformable self-attention is used to extract long-term feature dependencies. Finally, we design a context feature fusion module to efficiently aggregate the spatial information of the shallow network and the channel information of the deep network, and enhance the cross-layer features extracted by the network. Experiments demonstrate the effectiveness and robustness of this method.