Effective pavement skid resistance measurement using multi‐scale textures and deep fusion network

Abstract

AbstractPavement 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.