## 3 Experimental Results and Discussion
### Experimental Results
#### 3.3.3 Performance Data Visualization  
The following comparison of prediction results is shown in the figure, where it can be seen that the model YOLO-SUMAS accuracy is overall better than the baseline model YOLOv8 accuracy.  
Figures 10 and 11 show the Precision-Recall curves for the YOLOv8 and YOLO-SUMAS models for different defect detection categories, along with the corresponding average precision ([EMAIL_ADDRESS]) values. As shown in Figure 10, the YOLOv8 model shows high precision and recall in detecting defects such as open circuits, short circuits, spurious copper, missing holes, and mouse bites. Among them, the detection performance of open and missing holes is particularly outstanding, with precision and recall close to or over 0.98. However, for the detection of spurious copper and mouse bites, the performance of YOLOv8 is relatively weak, with a precision and recall of 0.905 and 0.951, respectively. In contrast, the YOLO-SUMAS model in Figure 11 shows higher performance in all types of defect detection tasks. The precision and recall of all defect categories are close to or over  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **P (\%)** & **R (\%)** & **FPS** & **F1** & **Params** & **GFLOPs** & **mAP@50** & **Open** (\%)** & **Spurious** & **Missing** (\%)** & **Hole** & **Mouse** (\%)** & **Spur** (\%)** \\ \hline YOLOv8n & 97.6 & 90 & 338.96 & 0.93 & 3.01 & 8.1 & 94.4 & 99.5 & 96 & 90.5 & 98.8 & 95.1 & 86.7 \\ \hline YOLO-SUMAS & 98.8 & 99.2 & 383.46 & 0.99 & 3.04 & 8.2 & 99.1 & 99.2 & 98.6 & 99.1 & 99.2 & 99.3 & 99.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of data before and after model improvement.  
0.98, especially for the detection of short circuits, spurious copper, and mouse bites; the precision and recall of YOLO-SUMAS reach 0.986, 0.991, and 0.993, respectively.  
According to Table 4, we compare and analyze the performance of YOLOv8 and YOLO-SUMAS in detecting PCB defects. Figures a to j show the comparison of defect types between the baseline model and the improved model, respectively, and it can be seen that the defect detection points are uniformly distributed and the confidence level is high. In the baseline model, the confidence level of the six defect detections is mainly located in 0.7 to 0.8, while the confidence level of the improved model for the six defect detections is mainly concentrated in 0.8 to 0.9, which is an improvement of about 10%. Figures m to x show the comparison of the heat maps of the baseline model and the improved model, respectively, and it can be seen that the distribution of the heat of the baseline model is relatively broad compared to that of the YOLO-SUMAS model, which is not as concentrated as the YOLO-SUMAS model. In summary, YOLO-SUMAS shows high accuracy and stability in detecting PCB defects and can provide high-quality results both in the detection of structural defects (e.g., open circuits, missing holes, and spurs) and functional defects (e.g., short circuits, spurious copper, and mouse bites).  
Figure 11: YOLO-SUMAS accuracy–recall curve [EMAIL_ADDRESS].  
Figure 10: YOLOv8n accuracy–recall curve [EMAIL_ADDRESS].