# Improved U-Net++ with Patch Split for Micro-Defect Inspection in Silk Screen Printing
## 5 Conclusions  
In this study, an improved U-Net++ model applying the patch-split method is proposed. When using an existing original image as it was, it failed to detect fine defects, and even an area that was not defective was registered as defective. By applying the proposed patch-split method, it could be seen that the dice score was improved by 0.67 or more when compared to previous studies. In addition, a Dice score of 0.728 was achieved by learning through a complex loss function rather than a single loss function. In this study, detection performance was improved through the pretreatment of data with small defects in the product that are difficult to see under light.  
In the era of the fourth industrial revolution, various convergence technologies have been applied to the manufacturing industry. However, there are many cases in which the defects of the product are irregular or too fine to be located because it is difficult to determine the characteristics in an image. However, in the future, through our research, it will be possible to fully detect products with fine defects compared to the size of the product, such as silkscreen prints. The proposed detection process is also expected to improve the consistency of data collection and processing. In addition, it was shown that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Loss Function** & **Dice Score** \\ \hline U-Net & BCE & 0.6729 \\ FPN & BCE & 0.679 \\ DeepLabV3 & BCE & 0.6729 \\ U-Net++ & BCE & 0.6729 \\ U-Net++ & Focal & 0.6729 \\ U-Net++ & Tversky & 0.7185 \\ FPN & Tversky + Focal & 0.7071 \\ (Our Proposal) U-Net++ & Tversky + Focal & 0.728 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results.  
Figure 21: Image detected by patch-split method and multiple loss functions.  
quality inspection using patch-split method-based AI is possible, even in situations where there is very little prior data on defective products, which is a common occurrence in the manufacturing industry.  
Small defects were detected well with the proposed method, but dust generated by the inspection equipment and workers was also detected; therefore, the inspection system needs to be improved. It appears that the weakness of the model can be supplemented through additional learning on collected data with dust to classify it into defects.  
In future research, to complement the weaknesses of the current research, the data collected in the field will be auto-labeled through data-centric MLOps to address model changes of manufacturing sites through auto-training. In addition, a comparison of the results with the current state-of-the-art models will be performed to prove the superiority of the proposed model [39].  
Conceptualization, B.Y. and J.J.; methodology, B.Y. and H.L.; software, B.Y. and H.L.; validation, B.Y., H.L. and J.J.; formal analysis, B.Y. and H.L.; investigation, B.Y.; resources, J.J.; data curation, B.Y.; writing--original draft preparation, B.Y.; writing--review and editing, J.J.; visualization, B.Y.; supervision, J.J.; project administration, J.J.; funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT (MIST), Korea, under the ICT Creative Consilience Program (IITP-2022-2020-0-01821) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1F1A1060054).  
Not applicable.  
This research was supported by the SungKyunKwan University and the BK21 FOUR (Graduate School Innovation) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).  
The authors declare no conflict of interest.