Robust Segmentation of Concrete Road Surfaces via Fuzzy Entropy Modelling and Multiscale Laplacian Texture Analysis

Accurate identification of concrete surfaces on roadways is critical for the advancement of autonomous navigation systems and the effective monitoring of transportation infrastructure.Nevertheless, the inherently heterogeneous texture of concrete, in conjunction with environmental variables such as lighting fluctuations and surface degradation, continues to impede precise surface segmentation.To address these challenges, a novel framework has been developed that integrates Fuzzy Topological Entropy (FTE) with Multiscale Laplacian Structural Dissimilarity (MLSD) for the robust delineation of concrete regions in road imagery.Within this framework, FTE is employed to model uncertainty and spatial ambiguity through a continuous fuzzy membership function, thereby capturing the nuanced transitions between concrete and non-concrete domains.Concurrently, MLSD is utilised to quantify multiscale structural irregularities by leveraging Laplacian-based texture dissimilarity, enhancing sensitivity to surface roughness and material inconsistencies.These complementary components are embedded within a unified energy functional, the minimisation of which is conducted via an iterative optimisation strategy that avoids the need for extensive training datasets or prior scene annotations.The proposed methodology demonstrates strong resilience across a variety of environmental conditions, including shadows, glare, occlusions, and physical wear.Superior performance is observed particularly in complex or degraded urban settings, where conventional segmentation models often fail.Owing to its non-parametric nature and computational efficiency, the approach is well-suited for real-time deployment in autonomous vehicle systems, smart city infrastructure, and road condition assessment platforms.By facilitating reliable and scalable surface segmentation without reliance on deep learning architectures or exhaustive manual labelling, this work offers a significant advancement toward generalisable and interpretable road surface analysis technologies.