## 3 Experiment and Analysis
### Results for Quality Assessment  
The comparison between the different methods on quality assessment is tabulated in Table 4. It can be seen that the detection performance of the SSD model is poor and clearly cannot meet the need for accurate detection. Compared to the SSD model, the Faster R-CNN performs much better, with a precision of 94.3% and a [EMAIL_ADDRESS] of 94.6%. However, its recognition speed is slow and cannot meet the demand of real-time detection. The precision, recall and [EMAIL_ADDRESS] values of the original YOLOv5 model are 99.98%, 100% and 99.5% respectively, reaching a very high peak with little room for improvement. While under the condition that the IoU threshold is set from 0.5 to 0.95, its [EMAIL_ADDRESS]:0.95 value is only 91.4%. To make the model focus more on the features helpful for quality assessment, we introduce an SE block into the original YOLOv5 model, namely the YOLOv5-Ours model. It can be observed that by adding the SE block, the YOLOv5-Ours gets a decent improvement of 5.4% and there is no increase in detection speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Quality Assessment** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **Times (s)** \\ \hline Faster R-CNN [32] & 94.3 & 88.7 & 94.6 & 82.1 & 0.098 \\ SSD [34] & 68.6 & 78.5 & 71.6 & 54.3 & 0.023 \\ YOLOv5 & 99.98 & 100 & 99.5 & 91.4 & 0.015 \\ YOLOv5-Ours & 99.98 & 100 & 99.5 & 96.4 & 0.015 \\ \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different methods on quality assessment.  
Figure 12: The comparison of ROI before (**a**) and after (**b**) the fine-adjusting.  
The results in Figure 13 are consistent with the conclusion drawn from Table 4. We can clearly observe from the comparison curves that as the iteration increases, the [EMAIL_ADDRESS] values of two models gradually stabilize and reach a similar maximum value. It should be noted that the [EMAIL_ADDRESS] curve of YOLOv5-Ours rises more steeply and peaks earlier, meaning it is easier to optimize after improvement. Another noteworthy finding is that the [EMAIL_ADDRESS]:0.95 curve of YOLOv5-Ours is consistently higher than that of YOLOv5 throughout the training process.  
From Figure 14, it can be clearly observed that the original YOLOv5 model can successfully detect and correctly classify solder joints, but the confidence of the detection results is not at a high level, ranging from 0.91 to 0.96. The reason for this may be that the distinction between normal and abnormal solder joints is not obvious, making it difficult for the original model to distinguish them with certainty. With the addition of SE block, our model can automatically learn more information that is useful for quality assessment, resulting in a better confidence score in the prediction, ranging from 0.96 to 0.98. Higher confidence means that the abnormal solder joints can be detected more accurately and have more reliable performance. Meanwhile, the quantitative results of quality assessment in Table 3 show that the YOLOv5-Ours model is almost unaffected and continues to perform well at both low and high light. The visual results in Figure 11 are also consistent with this, demonstrating that YOLOv5-Ours model is less sensitive to lighting changes. The above results show that by introducing the SE block, our model can be better adapted to the needs of engineering applications.