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

The accurate identification of combustion status can effectively improve the efficiency of municipal solid waste incineration and reduce the risk of secondary pollution, which plays a key role in promoting the sustainable development of the waste treatment industry. Due to the low accuracy of the incinerator flame combustion state recognition in the current municipal solid waste incineration process, 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, an efficient multi-scale attention module is introduced 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. In order to verify the effectiveness of the model proposed in this paper, comparative experiments and ablation experiments were conducted on the municipal solid waste incineration image dataset. The results showed that the Acc, Pre, Rec and F1 score indices of the model proposed in this paper were 96.16%, 96.15%, 96.07% and 96.11%, respectively. Experiments demonstrate the effectiveness and robustness of this method.