## 4 Experimental Results  
The goal of our experiment was to classify the objects into ten categories for grasping the manipulator. We applied a deep learning network for object classification. However, because the objects were not common items, it was difficult to collect sufficient training images to train the deep learning network. Therefore, using the proposed method, training images were generated to improve classification accuracy. There were 32 training images for each category. There were ten categories. The proposed data augmentation method increased the number of training images. In addition, transfer learning was applied for effective learning and to reduce the computational time because it uses a pretrained model that has already learned the features of numerous images. We selected VGGNet with 19 weights layers for transfer learning. VGGNet was already trained by the images of 1000 categories. We cut off and retrained the network using generated images by the methods [23; 27]. To verify the proposed method, we compared the classification accuracy of the network trained using the proposed method and the conventional methods. In addition, we trained and compared the deep learning network using the original data and data generated using the proposed method. The test results are presented in the tables. Test images were captured supplementally by placing the objects arbitrarily. They were not included in the training dataset. The classification accuracy is calculated by inputting test images into the trained model. Table 1 presents the classification accuracy of the network trained using original images. As abovementioned, there were 320 training images, 32 for each category. The experimental results show that the classification accuracy was low when noiseless test images were used because the original images were few. In general, the classification accuracy of noisy images was less than that of noiseless images. Thus, the classification accuracy of noisy images resulted in lower values. Table 2 presents the classification accuracy of the network trained by the conventional method using original images. Data augmentation was achieved by changing the range of color jittering. In general, color jittering is conducted by adding random values to a color channel. Based on this concept, color jittering #1 #3 add the random values in terms of RGB color space. Color jittering #1 adds positive values to the pixel values of an original image. Color jittering #2 and #3 add positive or negative values with different ranges [19]. When the conventional method was deployed, the number of training images increased by nine times, from 320 to 2880. The experimental results showed that the classification accuracy of the noisy images improved slightly. However, the classification accuracy tended to be low. Figure 7: Process of color perturbation for components of color space. To improve the classification accuracy, we eliminated the background and cropped an image by considering the center of the object. Table 3 presents the classification accuracy of the network trained using preprocessed images. The classification accuracy improved slightly over that of the original images and noisy images in Table 1.  
Next, we implemented a data augmentation method based on the inverse PSNR, with respect to the RGB color space and rotation. The number of training images increased by 216 times. Table 4 presents the classification accuracy of the network trained using the inverse PSNR, with respect to the RGB color space, and rotation of the images. As shown in the table, the performance of the trained network improved significantly and the classification accuracy was higher than that of the network trained using the original images and the preprocessed images. Specifically, the classification performance on the noisy images increased significantly. Furthermore, the classification performance on the noiseless images increased.