## 4 Experimental Results and Discussion
With a sufficient amount of training data, the data should be effectively augmented to train the network. Next, we applied the rotation method as another geometric transformation to the training dataset. The results are presented in Table 6.  
From Table 6, it is evident that applying the rotation method could significantly improve the classification accuracy. Because the objects were randomly laid down with respect to the rotation angle of the object in the conveyor environment, the rotation method was more influential than the flipping method. In this case, Experiment #6, which was related to overlapping the inverse PSNR method about two color spaces, had higher performance. In addition, we applied the flipping and rotation methods together, and the results are presented in Table 7.  
In the case of applying the flipping and rotation methods together, the performances of the trained networks slightly increased. In this case, the trained network of Experiment #4 by the training dataset \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Coor Perturbation** & **Geometric Transformation** & **Number of Training Data** & **Classification Accuracy** \\ \hline
1 & RGB PSNR & Rotation & 103,680 & 84.0\% \\
2 & Lab PSNR & Rotation & 115,200 & 91.0\% \\
3 & HSV PSNR & Rotation & 115,200 & 89.0\% \\
4 & RGB PSNR + Lab PSNR & Rotation & 218,880 & 92.0\% \\
5 & RGB PSNR + Lab PSNR + HSV PSNR & Rotation & 334,080 & 91.0\% \\
6 & RGB-Lab PSNR & Rotation & 1,036,800 & 93.0\% \\
7 & Lab-RGB PSNR & Rotation & 1,036,800 & 91.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification accuracy obtained upon applying the inverse PSNR method and Geometric Transformation #2: rotation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Coor Perturbation** & **Geometric Transformation** & **Number of Training Data** & **Classification Accuracy** \\ \hline
1 & RGB PSNR & Flipping & 11,520 & 77.0\% \\
2 & Lab PSNR & Flipping & 12,800 & 74.0\% \\
3 & HSV PSNR & Flipping & 12,800 & 65.0\% \\
4 & RGB PSNR + Lab PSNR & Flipping & 24,320 & 82.0\% \\
5 & RGB PSNR + Lab PSNR + HSV PSNR & Flipping & 37,120 & 65.0\% \\
6 & RGB-Lab PSNR & Flipping & 112,500 & 70.0\% \\
7 & Lab-RGB PSNR & Flipping & 112,500 & 79.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy obtained upon applying the inverse PSNR method and Geometric Transformation #1: flipping. adding up the two training data generated by the inverse PSNR method with respect to the RGB color space and CIE L*a*b* color space and performing the flipping method (horizontal, vertical, horizontal-vertical) had higher performance than those of others, as in Tables 3 and 5. Finally, we performed additional experiments by using an equal amount of training data. The smallest number of the generated training images was 1152 in the case of applying the color perturbation based on the inverse PSNR method with respect to the RGB color space and flipping transformation. Therefore, one-thousand training images were arbitrarily extracted in each category and used for training the network in the same manner as before. The experimental results are presented in Table 8.  
Because the amount of training data was reduced, the results of the classification accuracy tended to be slightly different than before. The experiment with the best performance with respect to average classification accuracy, as shown in Table 8, was the one in which the inverse PSNR method was applied in terms of overlapping CIE L*a*b* and RGB color space and rotation. The average classification accuracy was 92.0%, and the variance of the classification accuracy calculated using (17) was four. The experiment in which the inverse PSNR method was applied in terms of RGB and CIE L*a*b* color spaces and rotation achieved the second best performance. The average classification accuracy was 91.7%, and the variance of the classification accuracy calculated using (17) was 2.3.