###### Abstract  
The channel attention mechanism is widely used in deep learning. However, the existing channel attention mechanism directly performs the global average pooling and then full connection for all channels, which causes the local information to be ignored and the feature information cannot be reasonably assigned with the proper weights. This paper proposed a local channel attention module, based on the channel attention. This module focuses on the local information of the feature image, obtains the weight of each regional channel through convolution, and then integrates the information, so that the regional information can be fully utilized. Moreover, the local channel attention module is combined with the residual module, and the local channel attention residual network LSERNet is constructed to detect the abnormal state of the blast furnace tugree image. With sufficient experiments on the collected datasets of the blast furnace tugree, the results show that the proposed method can efficiently extract the feature information, and the recognition accuracy of the LSERNet model reached 98.59%. Further, our model achieved the highest accuracy, compared with SE-ResNet50, ResNet50, LSE-ResNeXt, SE-ResNeXt, and ResNeXt models. convolutional network; blast furnace tugree; residual network; attention mechanism; tugree image; image recognition +
Footnote †: journal:  
a hybrid module BAM (bottleneck attention module) that combines channel and spatial attention. The module is divided into two convolution parts and connects the two kinds of attention in parallel, the channel part is the SENet network structure, and the spatial part is the four-layer convolution, similar to the bottleneck structure. Finally, the two parts were integrated and the feature information weights were generated. Although this module improves the performance of the model, the complexity of the model is greatly increased, due to the four-layer convolution of the spatial attention part. In view of this defect, [PERSON] et al. [21] improved the BAM module and proposed an improved hybrid attention module CBAM (convolutional block attention module), which changed the original parallel mode of the channel attention and spatial attention to the serial mode. Moreover, a layer of global max pooling was added to SENet, two fully connected layers were removed and two convolutional layers were added. Firstly, global pooling was used to reduce the channel dimension, and then a layer of convolution was applied to extract the spatial information, which greatly reduced the computational burden of the model. However, CBAM [21] adds two pooling operations, which improve the performance of the visual attention mechanism and increase the complexity of the model. Although these methods have achieved a higher accuracy, they usually bring a higher model complexity and bear a heavy computational burden. [PERSON] et al. [22] proposed a grouping spatial attention module, named SGE (spatial group-wise enhance). The module divides the channel, so that each part of the division contains the feature information at different locations. Then it performs the spatial attention convolution on each part. The module bears a heavy computational burden. [PERSON] et al. [23] proposed a grouped hybrid attention module SA-Net (shuffle attention). The module first groups the channels of the feature map in a certain proportion, and then bisects the grouped sub-features, according to the channels, and performs channel attention and spatial attention, respectively. Finally, the output is integrated to obtain the final feature map of interest. The module increases the complexity of the model. In addition, [PERSON] et al. [24] proposed ECA-Net (efficient channel attention networks), which integrate the channel information by using a properly sized one-dimensional convolution. Although the computational cost is reduced, the information interaction in each dimension is ignored.