## 4 Experimental Results and Analysis
### Mask Wearing Detection  
Table 3 presents the average detection accuracy([EMAIL_ADDRESS]) of the original YOLOv5 model for all three categories: incorrect mask-wearing, correct mask-wearing, and no mask. The corresponding [EMAIL_ADDRESS] scores for these categories are 66.72%, 92.04%, and 73.28%, respectively. The mean average precision ([EMAIL_ADDRESS]) is 77.35%. Once the spatial-frequency fusion module was incorporated into YOLOv5, [EMAIL_ADDRESS] was improved to 71.36%, 92.22%, and 73.56% for all three categories, respectively, and [EMAIL_ADDRESS] was increased to 79.05%. A comparison of the original YOLOv5 and its variant suggests improvements in [EMAIL_ADDRESS] were 6.95%, 0.20%, and 0.38%, respectively, and [EMAIL_ADDRESS] increased by 2.20%. These results highlight the significant contribution the spatial-frequency fusion module has made to the overall performance, particularly in spotting incorrect mask-wearing. However, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Number of Parameters Introduced** \\ \hline SLPF-ResNet18 & 78.15 & \\ \hline +SE & 79.68 (+1.96\%) & 242 KB \\ \hline +CBAM & 79.74 (+2.03\%) & 252 KB \\ \hline +LA & 79.37 (+1.56\%) & 8 KB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results obtained by adding different attention mechanisms. Figure 15: Multiple models’ accuracy change curves on the test set. the introduction of frequency domain features resulted in a decrease in FPS. Therefore, when emphasizing real-time performance, we can use the original YOLOv5s, and when emphasizing accuracy, we can use the model proposed in this article. Figure 16 represents the PR curve for the YOLOv5 model, while Figure 17 represents the PR curve for the YOLOv5 model enhanced with spatial-frequency fusion. In these figures, the yellow, light blue, green, and dark blue lines correspond to the PR curves for no, correct, incorrect mask-wearing, and overall, respectively. The values at the top-right corner are the [EMAIL_ADDRESS] scores for each category. Figures 18 and 19 demonstrate the changes in various metrics, including loss values, precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS] during training for both the YOLOv5 model and its enhancement with the spatial frequency fusion. In these figures, \"train\" and \"val\" represent the training and the validation set, respectively. The red box in the graphs  
Figure 16: PR curve graph of the YOLOv5 model. Figure 17: PR curve graph of the YOLOv5 model with the spatial-frequency fusion module added. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**[EMAIL_ADDRESS] (\%)**} & \multicolumn{2}{c}{**[EMAIL_ADDRESS] (\%)**} & **FPS** \\ \hline \multicolumn{2}{c}{**Mask\_Weared\_Incorrect**} & \multicolumn{2}{c}{**With\_Mask**} & \multicolumn{2}{c}{**No\_Mask**} & \\ \hline original model & 66.72 & 92.04 & 73.28 & 77.35 & 105 \\ \hline our model & 71.36 (+6.95\%) & 92.22 (+0.20\%) & 73.56 (+0.38\%) & 79.05 (+2.20\%) & 98 \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental results of the original model and our model. indicates a sudden drop in precision and a corresponding rise in recall at around the 80 th epoch of training. This behavior stems from the initial high number of bounding boxes set by YOLOv5 for predictions. As the number of iterations increases, the number of bounding boxes decreases and object localization becomes more accurate, resulting in a sudden drop in accuracy while recall rises. We deployed the obtained model on mobile devices and tested it. The device has collected 1583 photos for testing, and the experimental results are shown in Table 4. It can be seen that the model [EMAIL_ADDRESS] is 80.31% and FPS is 22. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **FPS** \\ \hline
**Mask\_Weared\_Incorrect** & **With\_Mask** & **No\_Mask** & & \\ \hline
73.58 & 91.94 & 75.42 & 80.31 & 22 \\ \hline \hline \end{tabular}
\end{table}