Road markings constitute essential traffic control elements that ensure safety and traffic flow efficiency. The nighttime visibility of road markings, quantified through retroreflective luminance (RL), is fundamentally governed by the distribution characteristics of embedded glass beads (GBs) within the marking matrix. Yet, three persistent limitations hinder reliable GB distribution evaluation: measurement variability, oversimplified model assumptions (fixed 50% embedment depth vs. observed 50–60% variations), and fragmented correlations between GB morphology and RL metrics. This study proposes a granulometric–spatial–morphological triad assessment (GSMTA) framework, integrating instance segmentation with hierarchical performance analytics. The GSMTA framework achieves 15% higher segmentation accuracy over Otsu/Fast Random Forest methods, quantifying GB distribution via granulometric (size gradation), spatial (homogeneity index), and morphological (shape factor) descriptors. Through principal component analysis, the derived L3D performance indices establish statistically robust retroreflective luminance (RL) prediction models, with PC1 and PC2 capturing 91.7% of the variance and validation errors. The model remained below 8% error across a 750 mcd·m−2·lx−1 RL range, ensuring reliable and precise performance evaluation. Field validation demonstrates the framework’s capacity of transforming pixel-level segmentation data into practical quality control metrics. This advancement supports lifecycle management through standardized GB distribution evaluation, overcoming prior incompatibility issues between microscopic morphology analysis and macroscale RL measurements.
Lu et al. (Tue,) studied this question.