## 3 Experimental Results and Analysis
### Dataset Selection and Preprocessing  
This study primarily focused on six operational states of blast furnace tuyeres, namely normal, falling chunks, material block, hanging slag, coal break, and blowing down. We recorded multiple segments of videos using a camera installed at the tuyere, resulting in a total duration of 27 h. The videos were captured in MP4 format with a frame rate of 25 frames per second (FPS). Each operational state had a video duration of 270 min. To construct the dataset, we extracted one image from every 100 frames of each video segment, resulting in a collection of 24,300 images. Each image was assigned a corresponding class label based on its associated operational state. Prior to further analysis and model training, the dataset underwent the following preprocessing steps.  
1. To reduce computational complexity and prevent size distortion, the resolution of the blast furnace tuyere images was standardized from the original 960 \(\times\) 576 to 256 \(\times\) 256;
2. To address the issue of potential image mirroring caused by factors such as shooting angles or sensor installation methods in blast furnace tuyere images, this study applied horizontal or vertical flipping to the images. By correcting the image mirroring problem and aligning it with the actual scene, the diversity of the data was increased, which helped improve the model's generalization ability and enabled it to better adapt to images from different tuyere orientations or mirror scenarios;
3. Considering the presence of particulate matter or pollutants to a certain extent in a blast furnace tuyere, as well as the potential degradation of information transmission in the tuyere camera over time, this study randomly selected 4860 images (810 images per category) from the pool of 24,300 images and added salt-and-pepper noise to them. This random introduction of black and white pixels in the images simulated the loss or degradation of details in blast furnace tuyere images, making them more consistent with the actual conditions observed in tuyere environments.  
The blast furnace tuyere images dataset, named Tuyere-Data, consists of 4860 images with salt-and-pepper noise and the original 24,300 preprocessed images. Tuyere-Data encompasses six categories of working states: normal, falling chunks, material block, hanging slag, coal break, and blowing down, with 4860 images each. In total, the dataset comprises 29,160 images, with an example of each state depicted in Figure 7.  
To further validate the effectiveness of the BDiT model, we conducted experimental comparisons using two well-known public datasets for fine-grained image analysis: Stanford Dogs [(28)] ([[http://vision.stanford.edu/aditya86/ImageNetDogs/](http://vision.stanford.edu/aditya86/ImageNetDogs/)]([http://vision.stanford.edu/aditya86/ImageNetDogs/](http://vision.stanford.edu/aditya86/ImageNetDogs/)), accessed on 15 January 2023) and CUB-200-2011 [(29)] ([[http://www.vision.caltech.edu/datasets/cub_200_2011/](http://www.vision.caltech.edu/datasets/cub_200_2011/)]([http://www.vision.caltech.edu/datasets/cub_200_2011/](http://www.vision.caltech.edu/datasets/cub_200_2011/)), accessed on 15 January 2023). Table 1 provides details on the number of subclasses, the total number of images, and the train-test split for each dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Category** & **Total** & **Train** & **Test** \\ \hline Tuyere-Data & 6 & 29,160 & 23,328 & 5832 \\ Stanford Dogs & 120 & 20,580 & 16,464 & 4116 \\ CUB-200-2011 & 200 & 11,788 & 9431 & 2357 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the three datasets.