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Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.

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Pavement skid resistance measurement is a fundamental component of roadway management and maintenance. Most traditional approaches rely on manual operations or heavy devices, which lead to a labor-intensive, inefficient, and vulnerable testing environment. Precise laser scanning technology lays a solid foundation for effective and continuous pavement friction measurement. This paper proposed an automated pavement friction estimation model using 3D point cloud data and a deep neural network. The fine-grained texture data of over 800 pavement sections with various anti-skidding abilities were collected. The impact of the multi-scale textures on pavement friction was separated and analyzed via two-dimensional wavelet decomposition. A multi-input fusion network with deep aggregation modules was designed to fuse the features of sub-images generated by wavelet decomposition. The results show that the average prediction error is 0.0935, outperforming most state-of-the-art models. The impact of different texture scales on friction estimation is then revealed. The proposed method provides a new tool for effective and large-scale pavement friction evaluation.

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In ice and snow weather, the surface texture characteristics of asphalt pavement change, which will significantly affect the skid resistance performance of asphalt pavement. In this study, five asphalt mixture types of AC-5, AC-13, AC-16, SMA-13, SMA-16 were prepared under three conditions of the original state, ice and snow. In this paper, a 2D-wavelet transform approach is proposed to characterize the micro and macro texture of pavement. The Normalized Energy (NE) is proposed to describe the pavement texture quantitatively. Compared with the mean texture depth (MTD), NE has the advantages of full coverage, full automation and wide analytical scale. The results show that snow increases the micro-scale texture because of its fluffiness, while the formation of the ice sheets on the surface reduces the micro-scale texture. The filling effect of snow and ice reduces the macro-scale texture of the pavement surface. In a follow-up study, the 2D-wavelet transform approach can be applied to improve the intelligent driving braking system, which can provide pavement texture information for the safe braking strategy of driverless vehicles.

The pavement macro-texture and micro-texture are crucial factors for evaluating pavement performance as they have a significant correlation with friction, water film formation, and driving safety. During pavement construction, the macro-texture and micro-texture are significantly related to compaction operations. However, the current approach for evaluating pavement texture still relies on post-construction acceptance, with few considerations on the evolution patterns of pavement texture during the compaction process. Therefore, this study aimed to investigate the texture evolution law during compaction by implementing a laboratory compaction method. High-precision texture data from various asphalt mixtures were collected using 3D laser scanning during laboratory compaction. Macro-texture and micro-texture parameters were used to evaluate surface texture. Nineteen traditional geometric parameters were calculated at the macro-level to analyze macro-texture characteristics, while a 2D wavelet transform approach was applied at the micro-level to extract micro-texture, and the energy of each level and relative energy were calculated as indicators. This study analyzed the evolution law of parameters and found that certain parameters tend to converge. Moreover, geometric parameters and energy at lower levels of the samples could be utilized as supervising factors to regulate the compaction process.