## 2 Related Work
### Augmentation Policy Optimization for Data Imbalance Problem Solving
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Augmentation Policies** & **Detailed Description** \\ \hline
1 & MixUp & A policy that generates new data by randomly sampling and overlapping two input images. \\ \hline
2 & CutOut & A policy that randomly truncates some regions of the input image so that they are hidden only in the first layer of the CNN. \\ \hline
3 & CutMix & A policy that randomly samples two input images to create new data with a mixture of Ground Truth labels. \\ \hline
4 & Mosaic & A policy that combines four random-input images into one training image at a certain rate. \\ \hline
5 & Blurring & A policy that intentionally adds noise to the input image to blur the image. \\ \hline
6 & Brightness & A policy that adjusts the brightness of the input image to a certain proportion to convert it to dark or bright. \\ \hline
7 & Contrast & A policy that transforms contrast by adjusting contrast ratio of input images to a certain proportion. \\ \hline
8 & Hue & A policy that transform hues by adjusting hue ratio of the input image to a certain proportion. \\ \hline
9 & GrayScale & A policy that converts input images into black and white images to avoid considering color information. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Augmentation policies and description applicable to learning for object detection. Figure 5a-c show the augmentation policies for enhancing the performance of occluded objection detection that showed positive results when applied to the learning model in this study. Figure 5a CutMix is a policy that replaces a part of one image with a part of another image to newly configure the learning data, and shows amazing performance results for being an intuitive idea [35]. Figure 5b Mosaic is a policy that mixes and uses four learning images, and shows effective performance despite the small number of mini batches [36]. Figure 5c is a policy that processes the pre-existing color learning images into black-and-white images, and enables prompt image processing by simply delivering the luminosity information [37]. The MixUp, CutOut, Blurring, Brightness, Contrast, and Hue policies other than the three described above were not selected as the policies for model optimization, since the performance changes were reduced or insignificant in the transfer learning process. Accordingly, the CutMix, Mosaic, and Grayscale policies that showed detection performance enhancements through each policy performance evaluation were accepted and used in the final model learning. In addition, since the output value representing the detection performance varies depending on which activation function is used for model neural network configuration, it is very important to use activation functions suitable for learning [38]. In this study, a robust model suitable for normalization was developed and the Mish function that prevents overfitting was used as the activation function. This allows a bit of negative numbers to exist in the output value, and is particular in that the gradient is more smoothly displayed than that of the pre-existing ReLU series function. Since this is more advantageous in finding the global optimum value, it has a positive influence on the mean accuracy [38]. In addition, in the transfer learning process of the YOLO-based model, the k-means clustering method was applied to the bounding box of the ground truth included in the learning dataset, and the optimal anchor boxes suitable for crosswalk detection were found and used for learning. K-means clustering is an algorithm that aggregates given data into k clusters, minimizing the variance between each cluster and the distance difference. Generally, the method of using k-means clustering uses the euclidean method, but when applied to anchor boxes, it is difficult to find optimal anchor boxes because they rely too much on the center point. To improve this, we apply IoU(Intersection over Union) to extract anchor boxes.