# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 7 Experimental Result
### Discussion  
ResBlock is a module that combines a convolution layer and skip connection. ResBlock's skip connection transfers the features output from the convolution layer directly to the back layer, minimizing the loss of features that are not considered in the calculation of the convolution layer. We further describe the results of the ResBlock in Appendix B.  
The late-merge dual-stream CNN showed 10.1% higher performance due to differences in precision and a 5.3% higher F1-score. These improvements are due to the lack of high-level local features. In early-merge dual-stream CNN, the merge proceeds when the local feature for each solder region is not sufficiently extracted. In contrast, a late-merge dual-stream CNN proceeds with merging after extracting enough high-dimensional local features. A single-stream CNN extracts more local features than early-merge dual-stream CNN, but their defect classification performance is lower because the background regions are not excluded from the chip component image. The late-merge dual-stream CNN method showed 6.1% higher results in F1-score than SVM. This result seems to be due to the removal of the background region of the PCB, such as silk lines and patterns. The disadvantage of the previous machine-learning method is that the feature extraction region is fixed. As an improvement, the dual-stream CNN extracts core features related to defects by extracting solder regions having defect features concentrated on the convolution layer. Through this process, this paper achieved both a high defect classification performance and a reduction in network weight.  
In some samples, however, the dual-stream CNN did not correctly classify defects that were correctly classified by a single-stream CNN. In Figure 13, the entire chip component image can be  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Single-Stream** & **Late-Merge (Proposed)** \\ \hline \multirow{3}{*}{Inference Time (sample/ms)} & Image Transform & 1 & 3 \\ \cline{2-3}  & CNN & 7.7 & 5.4 \\ \cline{2-3}  & Total & 8.7 & 8.4 \\ \hline \multicolumn{2}{c}{Weight Memory (KB)} & 18,433 & 14,382 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inference time and weight of networks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MeasureMethod**} & \multicolumn{2}{c}{**Non-CNN Based**} & \multicolumn{2}{c}{**CNN-Based**} \\ \cline{2-5}  & **Decision Trees** & **SVM** & **MLP** & **Late-Merge (Proposed)** \\ \hline Average Accuracy (\%) & 45.6 & 88.4 & 84.9 & **96.0** \\ \hline Precision (\%) & 62.2 & 93.4 & 90.3 & **97.9** \\ \hline Recall (\%) & 45.6 & 88.4 & 84.9 & **96.0** \\ \hline F1-score (\%) & 52.6 & 90.8 & 87.5 & **96.9** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of classification by machine-learning methods.  
classified with the naked eye, but the solder region alone makes it difficult to classify the difference between the two defects. This phenomenon becomes more prominent with microchips and can be attributed to the small size of the chip component image. As the pixels of the camera are fixed, a smaller component will have a smaller image. Solder regions that can be obtained using low-resolution chip component images are limited, thereby limiting the defect information that can be obtained. Consequently, a small chip component image will make it harder to extract the solder region.