## 5 Experiments
### Experimental Results
#### 5.3.2 Combustion State Recognition  
To validate the accuracy and speed optimization of the method in the detection of combustion states in cement rotary kilns, we first conducted comparative experiments on different backbones to verify the superiority of the proposed method. The specific experimental results are shown in Table 5. Although FasterNet does not have a significant advantage in Params and FLOPs, it achieves excellent scores in latency, being 35.4 ms faster than the most widely used ResNet. Furthermore, compared to other lightweight networks such as MobileNet and ShuffleNet, FasterNet still achieves better results. MobileNet, ShuffleNet, GhostNet, and other networks use depthwise convolution (DWConv) and/or group convolution (GConv) to extract spatial features. However, in the process of reducing FLOPs, operators often suffer from the side effects of increased memory access. Additionally, these networks are often accompanied by additional data operations such as concatenation, shuffling, and pooling, which are often important for small models in terms of runtime. Our method improves the structure and receptive field of the network based on FasterNet. We upgraded the single-layer PConv to a double-layer and improved the receptive field from 3 \(\times\) 3 to 9 \(\times\) 9. Experimental results show that while there is no significant increase in parameters or computational complexity, the recognition accuracy is improved.  
After improvement, FasterNet can achieve an accuracy of 93.7% in recognition accuracy, but it still cannot meet the requirements of actual production. One reason is the lack of an attention mechanism in the original network. To address the issue of low recognition accuracy, we propose the introduction of a lightweight attention mechanism, DAS, to improve the network. This improvement not only ensures that the model is lightweight but also improves the recognition accuracy. The experimental results are shown in Table 6. The proposed improvement method increases the parameters by only 0.13 M and the FLOPs by only 0.01 G compared to the original network while improving the accuracy by 2.7%. We also compared it with other common attention mechanisms, such as SENet, BAM, and CBAM, demonstrating the significant superiority of the proposed improvement method.  
To validate the effectiveness of our improvement of the activation function in FasterNet, we replaced all ReLU activation functions in the network with FReLU visual activation functions and conducted experiments. The experimental results are shown in Table 7. Additionally, we compared it with other activation functions, such as ReLU, PReLU, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Params (M)** & **FLOPs (G)** & **Latency (ms)** & **ACC (\%)** \\ \hline None & 7.6 & 0.86 & 17.2 & 93.7 \\ +SENet & 7.78 & 0.86 & 21.5 & 95.3 \\ +BAM & 7.73 & 0.87 & 20.4 & 94.9 \\ +CBAM & 7.91 & 0.90 & 22.1 & 94.4 \\ +Triplet Attention & 7.78 & 0.88 & 20.5 & 95.1 \\ +DAS & 7.73 & 0.87 & 21.3 & 96.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental comparison of different attention mechanisms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Params (M)** & **FLOPs (G)** & **Latency (ms)** & **ACC (\%)** \\ \hline GhostNet & 7.3 & 0.23 & 20.1 & 92.3 \\ ShuffleNetV2 & 7.4 & 0.58 & 17.6 & 90.5 \\ MobileNetV2 & 6.1 & 0.58 & 21.4 & 91.3 \\ MobileVit-XS & 2.3 & 1.03 & 40.5 & 90.8 \\ ResNet-18 & 11.5 & 1.82 & 52.6 & 93.2 \\ FasterNet & 7.6 & 0.85 & 16.9 & 93.1 \\ Ours & 7.6 & 0.86 & 17.2 & 93.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental comparison of different backbones.  
Swish, demonstrating that the improvement proposed in this paper has a significant advantage in accuracy.