Evaluation of Asphalt Pavement Texture Using Multiview Stereo Reconstruction Based on Deep Learning

Abstract

To quickly obtain texture information and accurately evaluate the skid resistance of asphalt pavement, a multiview stereo reconstruction method based on deep learning is proposed for evaluating the texture depth of asphalt pavement. First, a depth camera and a digital camera are used to collect RGB-D (i.e., Red, Green, Blue, Depth) and RGB (i.e., Red, Green, Blue) multiview image datasets on different types of pavements for model training and validation respectively. Then, the model precision is validated by calculating the overlap (intersection over union, IoU) between the ground truth point cloud and the reconstructed point cloud. The model performances under different training strategies are compared to obtain the best model, and the effects of image resolution, number of views, and types of pavement material on the model precision are analyzed. Finally, image processing methods and texture depth characterization indexes are proposed to obtain pavement texture information from the reconstructed depth map, thus validating the effectiveness of the model. The results show that the trained model using the transfer learning strategy achieves a reconstruction precision of 0.77 when the image resolution is 3024×3024 and the number of views is 7. Furthermore, the reconstruction performance remains stable across different pavement materials, suggesting the model is suitable for accurately reconstructing depth maps for asphalt pavements. The errors between the predicted texture depth value calculated based on the volume-based index (MTDp) and the profile-based index (MPDp) using the depth maps after image processing and the measured texture depth value (MTDe) using the sand patch method are 11.72% and 18.85%, respectively. It is believed that the pavement texture depth can be effectively evaluated from the reconstructed depth map using the volume-based metric (MTDp). In contrast to traditional testing methods, this approach requires using only a digital camera and a personal computer, making it a lightweight and intelligent analysis method for obtaining pavement texture depth information.