## 3 Proposed Method  
As previously mentioned, it is difficult to collect a sufficient amount of training data to apply deep learning because the target object of the manufacturing domain is not generally an uncommon item. This results in a trained classifier that is vulnerable. Therefore, in this study, we propose a method of augmenting the training data to counter data deficiency. To compensate for the training data deficiency in the manufacturing domain, in this study, we propose a method of augmenting the data using a small dataset by changing the pixel value with respect to color spaces and transforming the image via rotation and flipping. The reason why we selected color perturbation is that the image is closely connected with luminance in the environment, and luminance has an influence on the color of the object in the image. The reason why we selected geometric transformation is that our experiments were conducted on a conveyor. Unlike the traditional method, which randomly adds the values to apply the color perturbation, the proposed method applies the color perturbation based on the decision of a reasonable range, which is determined by the inverse PSNR equation. Furthermore, in case of the traditional method, the input size of the training image for training the deep learning network is just resized by the input size of the network applied for the transfer learning. Therefore, the ratio of the image is distorted, and it results in decreasing the image classification accuracy. Therefore, the proposed method crops and resizes the image by maintaining the ratio of the object size in the original image. To implement this, the center point of the object in the image is calculated. Furthermore, to analyze the effect of the number of training images, the combination of the color perturbation with respect to each color space and the combination of the geometric transformation are conducted. The overview of the proposed method is depicted in Figure 2. The proposed method is divided into three parts: image pre-processing (background elimination; ROI extraction while maintaining the ratio of the  
Figure 1: Concept of transfer learning. original image size), color perturbation via a random Gaussian distribution based on the inverse PSNR, and geometric transformations. First, we capture the target objects that should be recognized by the deep learning network by using a vision sensor. In this study, the object images were comprised of 32 images for each category. Next, the background is eliminated to improve the classification accuracy by reducing the elements that have an influence on training the object. The objects for the image classification are laid on a color board. An object should be extracted from the image, without the color board as the background image. This step is shown in Figure 3.  
Figure 3: Process of background elimination. Figure 2: Overview of the proposed method. To eliminate the background color, the characteristics of the CIE L*a*b* color space are used. If the background color is white, shadows hinder the background removal process. Therefore, a background with a different color is considered. As previously mentioned, the CIE L*a*b* color space is comprised of the red-green axis and blue-yellow axis, as depicted in Figure 4. Figure 5 depicts each component of each color space. Red, green, blue, and yellow are more distinct in each component in the CIE L*a*b* color space than the RGB color space [35; 36]. Using this characteristic, we can easily extract the object from the background color. Because the colors of the object used for image classification are varied, such as red, green, blue, and black, the background colors of the board are determined as green and blue. The green and blue colors can be effectively extracted, as they have convenient values in the CIE L*a*b* color space that help distinguish the colors. The green color is located at the bottom of the a component, which is related to the red-green axis.