# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 5 Results and Discussion
### Comparison of the Results for Different Virtual-to-Real Ratios  
The results of pavement-distress detection for the YOLOv5 network fully trained on integrated datasets with six virtual-to-real ratios (0:1 to 5:1) are shown in Figure 11. Comparing the average precision of the YOLOv5 network on each integrated dataset, the results show that the larger the proportion of virtual disease data in the integrated dataset, the faster the MAP of the YOLOv5 network reaches its peak and stabilizes. The MAP of the YOLOv5 network is significantly increased by almost 30% when the ratio of virtual-to-real is 2:1, compared with the MAP with the ratio of 0:1 (65.10% vs. 39.5%).The MAP of the YOLOv5 network reaches an optimal value of 76.53% when the virtual-to-real ratio is expanded to 3:1. However, when the virtual-to-real ratio is further expanded to 4:1 and 5:1, the MAP gradually decreases, indicating that too much virtual data might generate a large number of redundant features, which results in the loss of the most discriminative and effective features and widens the difference between real pavement-distress features and virtual features, leading to a decrease in overall detection accuracy. Another data augmentation dataset based on traditional pre-processing such as rotation and mirroring is produced to compare with an integrated dataset with a virtual-to-real  
Figure 11: MAP for the integrated dataset of six virtual-to-real ratios. Figure 10: Performance comparison of different algorithms. ratio of 3:1 and an only real dataset, as shown in Table 4. The results show that image augmentation methods can effectively improve the accuracy of pavement-distress detection, among which the integration of the real and virtual data augmentation method outperforms the traditional data augmentation method. To be specific, the network trained on the integrated dataset with a virtual-to-real ratio of 3:1 achieves the AP of more than 50% for all five types of pavement distress (transverse cracks, longitudinal cracks, cross cracks, alligator cracks and potholes). In particular, the AP for pavement cross cracks achieves 96.97% and the MAP is 76.53%, which is twice that of the only real dataset without data augmentation (MAP: 39.5%) and 1.5 times that of the traditional data augmentation dataset (MAP: 46.9%). In summary, the YOLOv5 network trained on the integrated dataset with a virtual-to-real ratio of 3:1 enables obtaining more refined detection results for the identification of five types of asphalt pavement distress than with traditional data augmentation methods. When the IOU threshold is set at 0.5 and the confident threshold is set at 0.5, the Precision-Recall (P-R) curves and corresponding AP values of five types of asphalt pavement distress in different virtual-to-real ratios based on YOLOv5 model are shown in Figure 12a-e. The AP values for all five types of pavement distress rise correspondingly with the increase in the virtual-to-real ratio. The AP of the detection of transverse cracks, longitudinal cracks, cross cracks, and alligator cracks increases more than twice compared to the absence of virtual data, where the detection of cross cracks reached an accuracy of 96.97%. In addition, the AP for transverse cracks, longitudinal cracks and alligator cracks all peak at the maximum virtual-to-real ratio (5:1), while the AP for cross cracks and potholes peaks at the virtual-to-real ratio of 3:1, indicating that the features of these two distress features have a high complexity, low consistency and uncertainty. It is difficult for the virtual data to comprehensively and systematically reproduce all valid feature information in the real data, resulting in a decrease in detection accuracy when there are too many virtual data. In summary, the YOLOv5 network can obtain the best results when the ratio of virtual pavement-distress data to real asphalt pavement-distress data is 3:1.