## 4 Result and Performance Evaluation
This allows each gradient index to be applied as a same-level scale, and has a positive effect on understanding the model performance changes. If the involved index value is a positive number, this means that the mAP increases, and, if the involved index value is a negative number, this means that the mAP decreases. Accordingly, the detection performance changes were objectively evaluated through the involved gradient index values. Table 2 shows the detection performance results for various augmentation policies [29; 42]. As shown in Table 2, the CutMix policy showed the highest gradient value of 7.01. This showed better detection performance changes in comparison to the previously included MixUp and CutOut policies. CutMix is a policy created by mixing the strengths of the pre-existing MixUp and CutOut methodologies, and the objective performance difference was confirmed through the involved test results. The Mosaic policy showed the second highest gradient value of 6.21. Four random learning images were mixed into one image and were used for model learning, and it was confirmed that this policy had a positive influence on the learning efficiency and accuracy. Lastly, the Grayscale policy showed a high increase in terms of detection performance changes, and showed a gradient value of 4.08. The involved policy is a method that simply applies the black-and-white processing to the previously learned images. However, by examining the universal features of the learned images used in this study to detect crosswalk objects, it was possible to reason the grounds for high performance results. It was assumed that the difference between the black color of the asphalt-paved road and the white color displaying the crosswalk emphasized the features of the crosswalk through black-and-white processing and had a positive influence on the performance. Based on the test results, it was confirmed that the policies other than CutMix, Mosaic and Grayscale showed a mAP change that was either a negative number or low, so they were excluded from the optimal augmentation policy group. After completing the augmentation policy performance valuation, the optimal pol \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MU** & **CO** & **CM** & **MO** \\ \hline Inv.mAP & 0.7281 & 0.7355 & 0.7871 & 0.8360 \\ Pre.mAP & 0.7147 & 0.7281 & 0.7355 & 0.7871 \\ Gradient & 1.87 & 1.01 & **7.01** & **6.21** \\ \hline BL & **BR** & **CT** & **HU** & **GS** \\ \hline
0.8223 & 0.8380 & 0.8442 & 0.8392 & 0.8735 \\
0.8360 & 0.8223 & 0.8380 & 0.8442 & 0.8392 \\ \(-1.64\) & 1.90 & 0.73 & \(-0.6\) & **4.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection performance results for various Augmentation policies. MU: MixUp. CO: CutOut. CM: CutMix. MO: Mosaic. BL: Blurring. BR: Brightness. CT: Contrast. HU: Hue. GS: Grayscale. Inv.mAP: mAP after involving present policy. Pre.mAP: mAP before involving present policy. applied to the YOLO model for detection together with hard-example mining. Then, the performance evaluation involving the following models was conducted: the BASE+BASE model serving as the pre-existing base model, the HEM+BASE model to which hard-example mining was applied without augmentation policy optimization, the APO+BASE model to which augmentation policy optimization was applied without hard-example mining, and this study's HEM+APO model to which augmentation policy optimization and hard-example mining were applied. Table 3 shows the detection performance results for the occluded object detection model proposed in this study and for the pre-existing model [8,18]. As shown in Table 3, a comparison was made between the BASE+BASE model and the HEM+BASE model, and it was confirmed that HEM+APO model showed the highest mAP value of 90.49%. In addition, as shown in the F1-score performance index, the proposed model showed an F1-score value of 0.9027 and showed higher performance than the other three models that showed F1-score values of 0.7502, 0.8784 and 0.8568, respectively.