## 3 Results
### Experiments
#### 3.1.1 Dataset and Preprocessing  
In the early stage of the abnormal detection of the blast furnace tuyere, the videos were shot with a tuyere camera purchased from Beijing Shenwang Technology Co., Ltd. (Beijing, China) The camera device has a built-in CCD camera, which is placed outside the furnace mouth to take pictures of the situation inside the furnace. The blast furnace tuyere camera device is shown in Figure 5. The 167 videos, with a total length of about 26.5 h, of the blast furnace tuyere were collected. The videos were cut into pictures by frame, and the pictures were added with classification labels according to different abnormal states. The status of the blast furnace tuyere at work is mainly divided into six categories [7].  
1. Large chunks falling: large areas of darkening appear at the air outlet, and the large chunks are melted and disappear by the blast furnace, and the air outlet returns to brightness;  
Figure 4: Before and after the introduction of the RCB module: (**a**) traditional network; (**b**) residual network.  
Material block: gray coke in the blast furnace at the wind outlet for winding movement;
* Normal: the edge of the tuyere is smooth; there are no impurities in the tuyere and no dark area, and the dark area of the coal gun and coal injection is clearly visible;
* Coal breaking: the tuyere boundary is smooth, the tuyere is clear and bright, and there is no dark area produced by the coal injection;
* Slag hanging: there is uneven residue at the edge of the tuyere, and there is a small dark area at the slag hanging, and the gray level is large;
* Wind off: the coke rotation speed is slow, the carbon block gradually accumulates, and the wind is not bright and gradually darkens.  
The categories of the blast furnace tuyere images are shown in Figure 6.  
Figure 5: Blast furnace tuyere camera device.  
Figure 6: Blast furnace tuyere image category. (**a**) large chunks falling: large areas of darkening appear at the air outlet, and the large chunks are melted and disappear by the blast furnace, and the air outlet returns to brightness; (**b**) material block: gray coke in the blast furnace at the wind outlet for winding movement; (**c**) normal: the edge of the tuyere is smooth; there are no impurities in the tuyere and no dark area, and the dark area of the coal gun and coal injection is clearly visible; (**d**) coal breaking: the tuyere boundary is smooth, the tuyere is clear and bright, and there is no dark area produced by the coal injection; (**e**) slag hanging: there is uneven residue at the edge of the tuyere, and there is a small dark area at the slag hanging, and the gray level is large; (**f**) wind off: the coke rotation speed is slow, the carbon block gradually accumulates, and the wind is notbright and gradually darkens.  
The uneven amount of data obtained by each category will affect the training effect of the model. In order to obtain a better training effect, the following operations should be performed on the data before the model input [25].  
1. Expand the data to make the model have a strong generalization ability and to avoid over-fitting. The images are randomly rotated and flipped, and slight changes are added to enrich the dataset;
2. In order to reduce the impact of image noise, the region of interest (ROI) of the image is intercepted;
3. The image input size is 256 \(\times\) 256, which reduces the calculation amount of the model and speeds up the calculation speed.  
Therefore, the image dataset of the blast furnace tuyere after screening and expansion contains 21,120 pictures, including 3520 pictures of the falling block, material block, normal, broken coal, hanging slag, and rest wind, which are used for training and testing in the experiments.