## 5 Conclusions  
We developed a data augmentation method to overcome training data deficiency, achieve robustness against noise that causes classification errors, and improve the image classification accuracy. To examine the effect of the characteristics of the training images, we employed the adaptive inverse PSNR, which automatically determined the ideal perturbation range and weights for the color perturbation method, which were used to generate new training images. Weights are useful for classifying noisy images because the weights are greater than the color distribution. Experiments were performed to compare the classification models trained by the original data, the data generated by the conventional method, and the data generated by the proposed method using the test images. The experimental results showed that the images generated based on the adaptive inverse PSNR using the rotation method were more effective than the training data generated by the proposed method. In addition, the experimental results showed that the proposed method could generate new images as training data effectively, was robust against noisy images, and improved the image classification accuracy. In future, we plan to develop a data augmentation method that considers the characteristics of each image to overcome data deficiencies in the manufacturing domain. Further, we will apply the proposed method in other AI models such as object detection or semantic segmentation and so on. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Noiseless**} & \multicolumn{4}{c}{**Noisy**} \\ \hline  & **Precision** & **Recall** & **Specificity** & **F1-Score** & **Precision** & **Recall** & **Specificity** & **F1-Score** \\ \hline  & 0.75 & 0.30 & 0.99 & 0.43 & 1.00 & 0.20 & 1.00 & 0.33 \\  & 0.78 & 0.70 & 0.98 & 0.74 & 0.24 & 1.00 & 0.66 & 0.39 \\  & 1.00 & 0.10 & 1.00 & 0.18 & 1.00 & 0.10 & 1.00 & 0.18 \\  & 0.47 & 0.80 & 0.90 & 0.59 & 0.50 & 0.20 & 0.98 & 0.29 \\ Conventional Method & 0.30 & 1.00 & 0.74 & 0.47 & 0.47 & 0.80 & 0.90 & 0.59 \\  & 1.00 & 0.50 & 1.00 & 0.67 & 0.31 & 0.80 & 0.80 & 0.44 \\  & - & 0.00 & 1.00 & - & - & 0.00 & 1.00 & - \\  & 0.35 & 0.90 & 0.81 & 0.50 & 0.80 & 0.40 & 0.99 & 0.53 \\  & 1.00 & 0.10 & 1.00 & 0.18 & 0.33 & 0.10 & 0.98 & 0.15 \\  & 0.75 & 0.30 & 0.99 & 0.43 & 1.00 & 0.10 & 1.00 & 0.18 \\ \hline  & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\  & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.70 & 1.00 & 0.82 \\  & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.90 & 1.00 & 0.95 \\  & 0.67 & 1.00 & 0.94 & 0.80 & 0.36 & 1.00 & 0.80 & 0.53 \\ Proposed Method & 1.00 & 0.70 & 1.00 & 0.82 & 1.00 & 0.10 & 1.00 & 0.18 \\  & 0.88 & 0.70 & 0.99 & 0.78 & 1.00 & 0.50 & 1.00 & 0.67 \\  & 0.90 & 0.90 & 0.99 & 0.90 & 0.67 & 0.80 & 0.96 & 0.73 \\  & 1.00 & 0.90 & 1.00 & 0.95 & 1.00 & 0.70 & 1.00 & 0.82 \\  & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\  & 0.91 & 1.00 & 0.99 & 0.95 & 0.82 & 0.90 & 0.98 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Precision, recall, specificity, and F1-score of the conventional method and the proposed method. **Author Contributions:** Conceptualization, E.K. and H.L. ; methodology, E.K. ; software, E.K. ; validation, E.K., H.L. and J.K.; formal analysis, E.K. ; investigation, E.K. ; resources, E.K. and J.K.; data curation, E.K. and J.K.; writing--original draft preparation, E.K. ; writing--review and editing, S.K. ; visualization, E.K. and H.L. ; supervision, S.K. ; project administration, S.K. ; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Technology Innovation Program (10073147, Development of Robot Manipulation Technology by Using Artificial Intelligence) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.