# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
###### Abstract
[31] identified six types of damage quickly and accurately from a high-quality pavement distress dataset collected by UAVs based on a fully trained YOLOv3 network. This method adapted well to interference from environmental factors such as shadows, trees and pavement markings. [PERSON] et al. [32] transferred the R-CNN model to effectively identify and quantify cracks of bridges captured by UAVs. [PERSON] et al. [33] introduced the DenxiDeepCrack method to automatically detect pavement cracks based on UAV images by assessing high-level feature representations. [PERSON] et al. [34] proposed an image enhancement algorithm for the detection of road tunnel cracks, using road crack images to address the problem of insufficient image datasets for cracks on road tunnel surfaces. However, the accuracy and reliability of deep learning-based pavement-distress detection methods are highly dependent on the adequacy of large-scale, high-quality datasets, which often pose high acquisition costs [35]. In particular, the difficulty of obtaining timely negative samples for specific working conditions severely hampers the application of deep learning methods in automated pavement-distress detection [36]. Although some open- access datasets containing common pavement distresses provide sufficient negative samples, most of these datasets are high-quality images collected in the laboratory under specific scenarios, providing monotonous backgrounds, uniform illumination and clear imaging. However, existing deep learning models have to deal with some low-quality on-site acquisition data in practical detections. Interference scenario similar to cracks or potholes, uneven illumination and noise can significantly reduce the accurate assessment of distress from the background, and there is hardly an optimal deep-learning model that can extract the most discriminative features from low-quality images with limited negative samples to accurately identify cracks. Accordingly, this paper proposes a fine-grained pavement-distress detection method based on digital twin integrated data. The UAV-based asphalt pavement background model is first established; then a lightweight physics engine is used to render different types of pavement distress and specific working conditions on the background model, generating a digital twin model that reflects the distressed condition of the pavement, simulating the camera parameters and flight path of the UAV to obtain a virtual dataset with rich distress characteristics. These findings are finally mixed with the initial small sample dataset collected by the UAV in different proportions to obtain an integrated dataset. The integrated dataset is used to fully train the deep-object detection network to achieve refined detection of pavement distress under complex working conditions. The remainder of the paper is organized as follows: Section 2 critically reviews the techniques associated with the proposed approach. Section 3 systematically presents the framework and network structure of the proposed fine-grained method. Section 4 explains the experimental settings and evaluation metrics. Section 5 discusses the experimental results and compares and evaluates them with existing approaches, followed by the concluding remarks in Section 6.