## 3 Experimental Results and Analysis
### Comparative Experiment of Different Methods  
To evaluate the effectiveness of the BDiT model, several popular CNN models, including SERNet [34], ResNeXt [35], repVGG [36], ESRNet [24], and LSERNet [25], were trained on the Tuyere-Data dataset. A comparative analysis was conducted between these models and the BDiT model, and the experimental results are presented in Figure 8. The results of the experiments demonstrate that, with a minimal difference in the number of model parameters, the BDiT model achieved an accuracy of 97.86%. Compared to the SERNet model, the BDiT model showed an accuracy improvement of 0.85%. Compared to the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Method** & **Model** & **Teacher** & **Accuracy (\%)** \\ \hline (1) & ViT [13] & SA & - & 96.74 \\ (2) & ViT(EA) & EA & - & 96.80 \\ (3) & ViT(IEA) & IEA & - & 97.33 \\ (4) & ViT(IEA&SPA&L_DIS) & - & 97.52 \\ (5) & ViT(SA&L_DIS) & SA+L_DIS & - & 97.58 \\ (6) & ViT(IEA&SPA&L_DIS) & IEA+SPA+L_DIS & - & 97.86 \\ (7) & BDIT (IEA&SPA&L_DIS1) & IEA+SPA+L_DIS & VGG-19 & 97.31 \\ (8) & BDIT (IEA&SPA&L_DIS2) & IEA+SPA+L_DIS & ResNet-101 & 96.88 \\ (9) & BDIT (IEA&SPA&L_DIS3) & IEA+SPA+L_DIS & ResNet-50 & 97.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment results. ResNeXt model, the BDiT model exhibited an accuracy improvement of 1.17%. Moreover, the BDiT model outperformed the ESERNet model with an accuracy improvement of 1.38%. Among the models with a larger number of parameters, the BDiT model achieved an accuracy improvement of 1.12% compared to the repVGG model. Furthermore, compared to the LSERNet model, the BDiT model exhibited a marginal difference of less than 0.1% in accuracy. Taking both accuracy and the number of model parameters into consideration, the BDiT model demonstrated favorable performance compared to the other models under comparison. The confusion matrices of the BDiT model and LSERNet model on the Tuyere-Data test set, obtained from their respective training results, are presented in Figure 9.  
In the confusion matrix shown in Figure 9, the numbers 1 to 6 represent the classes normal, falling chunks, material block, coal break, hanging slag, and blowing down, respectively. The results indicate that coal break, hanging slag, and blowing down are relatively easy to detect among the five abnormal states of the tuyere. However, the detection efficiency for normal, falling chunks, and material block is comparatively lower. The LSERNet model achieves the best performance in detecting the normal class, but it is outperformed by the BDiT model in detecting falling chunks and material block. The BDiT model achieves the highest accuracy among the five abnormal states of the tuyere. We investigated the accuracy of the BDiT model for distillation parameters of 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, and 0.5, and the experimental results are shown in Figure 10.  
Figure 8: Average accuracy and parameters of different classification models. Figure 9: The confusion matrix of the model. It can be seen from the broken line trend in the figure that before the distillation parameter is set to 0.1, the accuracy of the model gradually increases with the increase of the distillation parameter, reaching the highest value of 97.86% at 0.1, and then, the accuracy decreases with the increase of the distillation parameter. It further proves that the experimental distillation parameter of 0.1 is the best choice in this paper. To evaluate the generalization ability of our proposed model, the BDiT model was trained and tested on the CUB-200-2011 dataset and the Stanford Dogs dataset. A comparative analysis was conducted with various other models, and the experimental results are summarized in Table 3.  
From the results in Table 3, it can be observed that on the CUB-200-2011 dataset, our proposed method achieved significantly higher accuracy compared to LSERNet, ResNet50, and other CNN-based methods.