# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 5 Experimental Result
### Defect Detection Performance According to the Number of Gabor Images  
The comparison was made according to the number of selected Gabor images. In Sections 5.2 and 5.3, it was confirmed that the network using the Gabor image performed better than the network using only the gray image. In addition, we compared the accuracy according to the number of inputs of Gabor images. All gray images are input, and only the number of Gabor images is adjusted to 2, 4, 6, and 8, and the learning epoch and learning rate are the same as the settings of Section 5.1.  
Table 8 shows the results of the experiments on the use of the MT dataset with different numbers of Gabor filter images selected. The single-stream structure had the highest accuracy when the Gabor filter image was 4 or 6, and the dual-stream structure had the highest accuracy when the Gabor filter image was 2 or 8. As a result of comparing the accuracy of single-stream and dual-stream in the same MT dataset, the accuracy of single-stream was higher. The blob of the MT dataset showed that the single-stream structure had higher accuracy. The above experimental results confirmed that many Gabor filters did not always have high accuracy and that the accuracy was increased when an image with good features was selected and used based on the IDM value. Unnecessary Gabor filter images acted as noise components in stain defect classification.  
Table 9 shows the results of the experiments on the use of the CCM dataset with different numbers of Gabor filter images selected. As a result of the experiment, both the Single-stream and Dual-stream showed the highest performance when two Gabor filter image was added. it was confirmed that using images with well-extracted IDM reference features is more efficient, rather than increasing accuracy by having many Gabor filter images. It was confirmed that the dual-stream structure has higher accuracy than the single-stream structure, albeit in a minute way.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Network Structure** & **Number of Gray Image** & **Number of Gabor Images** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Single-stream} & 1 & 2 & 95.4 & 98.4 & 96.9 \\  & 1 & 4 & 98.6 & 98.2 & 98.4 \\  & 1 & 6 & 96.9 & 99.2 & 98.0 \\  & 1 & 8 & 96.5 & 99.0 & 97.7 \\ \hline \multirow{4}{*}{Dual-stream} & 1 & 2 & 96.1 & 99.0 & 97.6 \\  & 1 & 4 & 95.1 & 96.6 & 95.8 \\ \cline{1-1}  & 1 & 6 & 95.4 & 98.6 & 97.0 \\ \cline{1-1}  & 1 & 8 & 95.8 & 99.4 & 97.6 \\ \hline \end{tabular}
\end{table}
Table 8: Performance according to the number of gapor images on magnetic tile dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Network Structure** & **Number of Gray Image** & **Number of Gabor Images** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Single-stream} & 1 & 2 & 99.6 & 98.4 & 99.0 \\  & 1 & 4 & 96.6 & 96.8 & 96.7 \\  & 1 & 6 & 96.6 & 96.2 & 96.3 \\  & 1 & 8 & 92.7 & 92.2 & 92.3 \\ \hline \multirow{4}{*}{Dual-stream} & 1 & 2 & 99.6 & 99.0 & 99.3 \\  & 1 & 4 & 97.8 & 97.6 & 97.7 \\ \cline{1-1}  & 1 & 6 & 94.5 & 94.2 & 94.3 \\ \cline{1-1}  & 1 & 8 & 89.5 & 93.0 & 91.1 \\ \hline \end{tabular}
\end{table}
Table 9: Performance according to the number of gapor images on compact camera module dataset.