## 4 Result and Performance Evaluation  
As far as the test environment for implementing the proposed occluded object detection model is concerned, the hardware and operating system consisting of Windows10 Pro, AMD Ryzen 5 1600 Six-Core Processor, NVIDIA GeForce RTX 2080 and RAM 16 GBwere used. In this study, a detection model of occluded object based on YOLO using hard-example mining and augmentation policy optimization was proposed. Figure 6 shows the crosswalk occlusion object detection results through the proposed hard-example mining. Figure 5(a),b shows the detection model results acquired by using Seoul road driving images as the test data, and Figure 5(c),d shows the detection model results acquired by utilizing the IARA data not used as learning data as the test data. Figure 5(a) shows the YOLO-based crosswalk detection results of a hard-negative example before the application of hard-negative mining. Based on the detection results, it was confirmed that the traffic information on a road that is not a crosswalk was incorrectly detected as a crosswalk, and that the confidence score value was 0.64 at that point. Figure 5(b) shows the detection results of a hard-negative example after the application of hard-negative mining, and it was confirmed that the confidence score value of the crosswalk was 0.25, and that the false-positive detection rate was greatly decreased. Figure 5(c) shows the crosswalk detection results of a hard-positive example before the application of hard-positive mining. The involved example was a representative occlusion phenomenon, and the confidence score values were 0.09 and 0.10, and it was unable to detect the crosswalk due to the block objects in front. However, as Figure 5(d) shows, based on the detection results acquired after the application of hard-positive mining, the confidence score values were enhanced to 0.90 and 0.89, and it was able to stably detect the objects that occluded the crosswalk in a road driving environment. The false-positive (64%) and false-negative(10%) objects generated in the pre-existing base model showed a false-positive detection rate of 25% and a true positive detection rate of 90%, respectively, meaning that the crosswalk object detection performance was greatly enhanced. As far as the real-life object detection is concerned, since the threshold value of the confidence score was 0.5, it was determined that the proposed model more stably detects crosswalk objects. To evaluate the performance of the occluded object detection model proposed in this study, the performance evaluation for optimal augmentation policy configuration and the performance evaluation of the YOLO-based model to which the optimized augmentation policy and hard-example mining were applied were performed. To enhance the crosswalk  
Figure 6: Crosswalk Occlusion Object Detection Results with Hard-example Mining. HNM: Hard-negative Mining. HPM: Hard-positive Mining. occlusion object detection performance during road driving, the performance changes in diverse augmentation policies were observed. The [EMAIL_ADDRESS] (mean Average Precision) was used as the performance index for evaluation, and the gradient value was used as the [EMAIL_ADDRESS]-based quantitative index. mAP is a value that expresses the precision values calculated based on the actual object-detected recall changes as one index, and is a representative performance evaluation index of an object detection algorithm [41; 42]. Equation (1) represents the gradient index. \[\textit{Gradient}\textbf{=}\textbf{{{{mAP}}}\textbf{{{\emph{after involving present policy}}}}}\times\textbf{{{100}}}\textbf{{{\emph{)}}}}\textbf{{{\emph{-100}}}} \tag{1}\]  
Equation (1) shows the relative changes in the mAP of the detection model before including the current augmentation policies and in the mAP of the detection model after including the current augmentation policies. At this point, the value is multiplied by 100 and 100 is subtracted from the value for the convenience of calculation through the normalization of performance changes.