## 3 Results  
Figure 7 showed a representative image of the thermographic spectrum corresponding to each test. In tests without face mask restriction, 100% is considered (see Figure 7a). In descending order, the 100% cotton face mask presented a 69% leakage (see Figure 7b); the 100% polyester face mask showed 60% leakage (see Figure 7c); the 3-layer pleated face mask had 39% leakage (see Figure 7); Figure 7e-g shows the face masks that presented the best performance, 98% cotton, neoprene, and KN95, with 26%, 25%, and 25% leakage, respectively. Figure 7h shows the thermal spectrum of the ideal test where there was no leakage. The results were congruent with those presented in related studies [18; 30].  
The YOLO v4 object detection network trained with the data set previously mentioned in Section 2.5 was used to verify the proposed method's efficiency; the 311 images from the test set were analyzed. Data obtained from the mAP showed 1143 detections, and 311 unique truth values were taken into account. Figure 8 shows the loss and mAP curve during training. In the initial stage of training, the model's learning efficiency was higher, and the speed of convergence of the training curve was fast. As the training increased, the slope of the loss curve gradually decreased. Finally, when the number of training iterations reached around 5000, the model's learning efficiency reached convergence, and the loss fluctuated around 0.5. Table 4 showed each category's testing performance metrics  
Figure 7: Representative thermographic spectrum images of each test captured by the imaging system. (**a**) Without a mask, 100% leakage; (**b**) 100% cotton, 69% leakage; (**c**) 100% polyester, 60% leakage; (**d**) 3-layer pleated, 39% leakage; (**e**) 98% cotton, 26% leakage; (**f**) neoprene, 25% leakage; (**g**) KN95, 25% leakage; (**h**) mask without any leak, 0% leakage.  
within the data set. Examining the confusion matrix for the performance metrics of the classification task used (Figure 9), it can be seen that one category of leakage percent was classified with a precision of 100% and one with precision more significant than 90%. The two worst-performing categories, 60%, and 26% had the most errors, including confusion between neighboring categories.  
For a minimum threshold of 0.25, a precision of 0.815, a recall of 0.842, and an F1-score of 0.821 were obtained. The proposed model correctly classified 255 images, only 56 prediction errors, and had an IoU average of 58.06%. The mean average precision ([EMAIL_ADDRESS]) for the data set was 0.9345 or 93.45%.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Category** & **n (Truth)** & **n (Classified)** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
100\% & 40 & 42 & 98.71\% & 0.93 & 0.97 & 0.95 \\
69\% & 45 & 45 & 96.78\% & 0.89 & 0.89 & 0.89 \\
60\% & 58 & 43 & 92.6\% & 0.91 & 0.67 & 0.77 \\
39\% & 34 & 41 & 93.89\% & 0.68 & 0.82 & 0.75 \\
26\% & 47 & 43 & 92.28\% & 0.77 & 0.70 & 0.73 \\
25\% & 71 & 75 & 91.64\% & 0.80 & 0.85 & 0.82 \\
0\% & 16 & 22 & 98.07\% & 0.73 & 1.0 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Testing performance metrics per category.  
Figure 8: Loss and mAP curve of the YOLO v4 model.