# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 6 Conclusions  
This paper proposes a pavement-distress fine-tuned detection method based on a digital twin model and a deep-object detection algorithm, which creatively enables accurate and efficient detection and classification of five types of pavement distress and obtains good results in adverse scenarios such as shadow, occlusion and blur. Compared to traditional data augmentation methods, the virtual data augmentation method based on a digital twin model improves detection accuracy and yields positive general results. This method effectively addresses the challenges of low detection accuracy, high deployment cost, and poor adaptability caused by the lack of high-quality pavement distress samples. From our experiments, we can draw several conclusions. (1) The fine-grained pavement-distress detection method using a digital twin model is proposed. Through pavement background modelling based on UAV and a virtual disease rendering based on the lightweight physics engine, a digital twin model capable of simulating the damaged state of the pavement is generated which can reproduce the pavement distress features realistically and simulate a variety of pavement scenarios. The virtual data generated by the digital twin model and the real data are integrated to fully train deep-object detection networks. The integrated dataset produces high-quality pavement disease samples, improving detection accuracy and the robustness of the model and reducing deployment costs. This effectively addresses the challenges of low detection accuracy, high deployment cost and poor adaptability caused by the lack of high-quality pavement-distress samples. (2) The YOLOv5 network is fully trained with an integrated dataset with six virtual-to-real ratios, among which the model with a virtual-to-real ratio of 3:1 achieves the optimal detection results for asphalt pavement-distress detection, with the MAP of 76.53% for transverse cracks, longitudinal cracks, cross cracks, alligator cracks and potholes, which is twice that without virtual data (MAP: 39.5%) and 1.5 times that with traditional data augmentation (MAP: 46.9%). The larger the proportion of virtual data in the integrated dataset, the faster the MAP of the YOLOv5 network reaches its peak and stabilizes. However, when the virtual-to-real ratio exceeds 3:1, the non-critical redundant features are generated, which influences the feature extraction ability of the model, leading to a loss of the most discriminative and effective features and a reduction in detection accuracy. Furthermore Furthermore, training the network model using 100 real images is sufficient for the model to extract the most discriminative features of the pavement distress images. (3) This paper further tests the adaptability and generalization of the YOLOv5 network trained with virtual data augmentation in terms of different detection scenarios and different pavement texture. The YOLOv5 network fully trained with an integrated dataset with the optimal virtual-to-real ratio (3:1) achieves a recall of over 40% for all five pavement distress types in shadow, occlusion and blur scenarios, functionally improving the effectiveness and adaptability of complex pavement-detection scenarios and reducing the missed detection rate of distress compared to the training results without virtual data. When virtual data is applied to concrete pavement scenarios, the best detection result (MAP of 68.31%) is obtained in the concrete pavement distress test set when the virtual-to-real ratio is 3:1, demonstrating the general applicability of the virtual data augmentation method and the 3:1 virtual-to-real ratio. Funding acquisition, supervision, W.W.; Writing--original draft, methodology, X.X. ; Investigation, visualization, J.P.; Software, supervision, writing-Review, W.H. ; Data curation, D.W. All authors have read and agreed to the published version of the manuscript. This research was funded by High-Speed Railway Infrastructure Joint Fund of the National Natural Science Foundation of China, No.