Multi-Scale Quality Evaluation of Road Markings from Glass Bead Distribution: A Novel GSMTA Framework

Road markings constitute essential traffic control elements that ensure safety and traffic flow efficiency. The nighttime visibility of road markings, quantified through retroreflective luminance (R_L), 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 R_L 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 (R_L) 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^-1R_L 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 R_L measurements.