## 5 Conclusions

Table 8: Classification accuracy obtained upon applying the inverse PSNR method and two geometric transformations with an equal number of training images. + Lab PSNR, flipping with RGB PSNR + Lab PSNR + HSV PSNR, flipping with RGB-Lab PSNR, flipping with Lab-RGB PSNR, rotation with RGB PSNR, rotation with HSV PSNR, rotation with RGB PSNR + Lab PSNR, rotation with RGB PSNR + Lab PSNR + HSV PSNR, rotation with RGB-Lab PSNR, rotation with Lab-RGB PSNR, flipping and rotation with RGB PSNR, flipping and rotation with Lab PSNR, flipping and rotation with HSV PSNR, flipping and rotation with HSV PSNR, flipping and rotation with RGB PSNR + Lab PSNR, flipping and rotation with RGB PSNR + Lab PSNR + HSV PSNR, flipping and rotation with RGB-Lab PSNR, flipping and rotation with Lab-RGB PSNR. The experimental results showed that the RGB PSNR + Lab PSNR with rotation and flipping method showed better performance in the training data generated by the proposed method. The classification accuracy of the network trained using the generated dataset by color perturbation based on the similarity calculation, which was related to our contribution, showed better performance than the arbitrary color perturbation like the traditional methods. In general, the numerous image data showed a better performance of the classification accuracy. However, it was not absolute. Therefore, we plan to study effective data augmentation methods by evaluating the appropriate numerical value for improving the performance of the classification accuracy. The experimental results showed that the proposed method could improve the classification accuracy by classifying similar objects, which were easy to misclassify. This indicated that color perturbation based on similarity was effective at improving the classification accuracy. In future work, we plan to develop other reasonable data augmentation methods to overcome data deficiency, including in the manufacturing domain. Conceptualization: E.K.K. and H.L. ; Data curation: E.K.K. and J.Y.K. ; Formal analysis: E.K.K. ; Funding acquisition: S.K. ; Investigation: E.K.K. and H.L. ; Methodology: E.K.K. ; Resources: E.K.K. and J.Y.K. ; Software: E.K.K. ; Supervision: S.K. ; Project administration: S.K. ; Validation: E.K.K., H.L. and J.Y.K. ; Visualization: E.K.K. and H.L. ; Writing (original draft preparation): E.K.K. ; Writing (review and editing): S.K. All authors have read and agreed to the published version of the manuscript. This research was funded by BK21 PLUS, Creative Human Resource Development Program for IT Convergence, Grant Number 1345311062. This research was supported by BK21 PLUS, Creative Human Resource Development Program for IT Convergence, and 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) and was funded and conducted under the Competency Development Program for Industry Specialists of the Korean Ministry of Trade, Industry and Energy (MOTIE), operated by the Korea Institute for Advancement of Technology (KIAT) (No. P0008473, The development of high skilled and innovative manpower to lead the Innovation based on Robot). The authors declare no conflict of interest.