# Improved U-Net++ with Patch Split for Micro-Defect Inspection in Silk Screen Printing
## 4 Performance Analysis
### Experimental Results  
First, the effect of the patch-split method was analyzed while performing training and inference. In the conducted experiment, the architecture was fixed as U-Net and the loss function was fixed as BCE loss. Figure 17 depicts the case of the original input. It was not possible to learn the characteristics of the image well. However, in the case of learning by applying the patch-split method, the performance steadily increased as the learning progressed. It was observed that the maximum Dice score was attained in epoch 3.  
The effects of the patch-split method are presented in Table 3. The original input had a very low Dice score of 0.001376, whereas the patch-split method had a Dice score of 0.6729.  
In the inference result image of Figure 18, even the non-defective parts were inferred as defects when using the original input. On the other hand, the defects were properly detected using our proposed patch-split method.  
The loss function was fixed as the BCE loss and was analyzed for each architecture. As shown in Table 3, the FPN showed the best performance with a Dice score of 0.679, similar to U-Net ++. As shown in Figure 19, when the FPN architecture was used, defects were detected, but the performance could have be better, and the segmentation area deviated slightly from the defect area. With the proposed method, all types of defects within the defect data set were detected.  
As shown in Figure 20, the Dice score of DeepLabV3 and FPN did not reach 0.7 from the start to the end of learning. However, in our proposed method, it was confirmed that the Dice score was stable at over 0.72 after completely learning in more than 2000 steps.  
Figure 17: Training log (patch-split and original size input).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Architecture** & **Loss Function** & **Dice Score** \\ \hline Original Input & U-Net & BCE & 0.001376 \\ Patch Split Input & U-Net & BCE & 0.6729 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of original input and patch-split input.  
As shown in Table 4, the difference was large for each loss function. The proposed loss function is a combination of focal loss and Tversky loss. In the experiment of the study \"A Novel focal Tversky loss function with improved attention U-Net for lesion  
Figure 19: Images detected using patch-split method and a single loss function.  
Figure 20: Training log (proposed method, DeepLabV3 and FPN).  
Figure 18: Images detected using original input and patch-split input.  
segmentation\" [38], the combined form of the two loss functions seemed to be more effective and was better than the Tversky loss function; therefore, we used it for the combined loss. The analysis was conducted using a combination of Tversky loss and focal loss, which are loss functions suitable for segmentation. As a result, the defect detection performance was better than that of the previous analysis, regardless of the architecture. The Dice score of our proposed U-Net++ architecture was the highest at 0.728.  
As shown in Figure 21, the proposed method only found defects and the segmentation area was correct.