# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 5 Conclusions and Future Works  
In this study, the automatic defect detection of backside of STS304 plate based on lock-in infrared thermography was performed. The concept of lock-in signal processing and the Otsu algorithm for binarization was reviewed. In addition, a research process for automatic defect detection was presented, and first and second de-noising were performed. SNR evaluation was performed after applying median filtering to remove noise from the signal processing image: the amplitude image improved by 2.67%, and the phase image improved by 18.22%. After applying the Otsu algorithm for binarization processing, second de-noising and boundary line tracking were performed for automatic defect detection. As a result of applying the roundness metric equation, more defects were detected in the phase image than the amplitude image. As a final process, RMSE was calculated to evaluate the detectability according to filtering.  
However, there were two major limitations in this study. First, a lot of noise was generated after the lock-in signal processing because uniform heat was not provided by using two halogen lamps. Secondly, the metric was a roundness equation, and there is a limit to the detection of circular defects. Although there were such limitations, this paper proposes that it is possible to detect circular defects in a short time by constructing a simple algorithm mechanism.  
In future research, an advanced noise removal technique that can improve the detectability for circular backside defects will be considered. In addition, a study to establish a mechanism capable of real-time automatic defect detection is required.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Hole**} & \multicolumn{2}{c}{**Amplitude**} & \multicolumn{2}{c}{**Phase**} \\ \cline{2-5}  & **Filtering** & **Non-Filtering** & **Filtering** & **Non-Filtering** \\ \hline A1 & \(-\) & \(-\) & \(88\) & \(79\) \\ A2 & \(-\) & \(-\) & \(-\) & \(-\) \\ A3 & \(-\) & \(-\) & \(80\) & \(85\) \\ A4 & \(-\) & \(-\) & \(81\) & \(84\) \\ B1 & \(87\) & \(84\) & \(86\) & \(85\) \\ B2 & \(-\) & \(-\) & \(-\) & \(-\) \\ B3 & \(81\) & \(79\) & \(82\) & \(77\) \\ B4 & \(88\) & \(88\) & \(84\) & \(82\) \\ C1 & \(81\) & \(84\) & \(88\) & \(85\) \\ C2 & \(-\) & \(-\) & \(-\) & \(-\) \\ C3 & \(79\) & \(-\) & \(-\) & \(-\) \\ C4 & \(92\) & \(89\) & \(86\) & \(80\) \\ D1 & \(86\) & \(85\) & \(88\) & \(84\) \\ D2 & \(-\) & \(-\) & \(-\) & \(-\) \\ D3 & \(83\) & \(86\) & \(84\) & \(84\) \\ D4 & \(92\) & \(91\) & \(89\) & \(84\) \\ \hline RMSE & 23.657 & 31.1469 & 23.6378 & 24.8919 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE values for the filtering and non-filtering of amplitude and phase images.  
**Author Contributions:** Conceptualization, S.L., Y.C., and W.K.; methodology, S.L., Y.C. and R.S.; software, S.L. and Y.C.; validation, S.L., Y.C. and R.S.; formal analysis, Y.C. and R.S.; investigation, S.L., Y.C., R.S. and W.K.; resources, W.K.; data curation, R.S.; writing--original draft preparation, S.L.; writing--review and editing, W.K.; visualization, Y.C.; supervision, W.K.; project administration, W.K.; funding acquisition, W.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF-2019R1F1A 1061328) funded by the Korean government, Ministry of Education, Science and Technology (MEST).  
**Infrared Consent Statement**: Not applicable.  
**Data Availability Statement**: Due to the nature of this research, participants of this study did not agree for their data to be shared publicly and only available upon reasonable request.  
The authors declare no conflict of interest.