Automated Road Marking Wear Assessment via Multimodal Fusion of LiDAR Intensity and YOLOv11 Semantic Segmentation.

Road markings support lane guidance, traffic regulation, and machine perception, but their field inspection still depends largely on manual surveys or local retroreflectivity measurements. This study presents a vehicle-mounted inspection framework that fuses LiDAR intensity with camera-based semantic segmentation for automated road marking wear assessment. The platform integrates LiDAR, a stereo camera, IMU, GNSS, and an industrial computer. LiDAR-inertial mapping provides spatial alignment, while ground filtering, region-of-interest extraction, and adaptive intensity thresholding generate preliminary marking candidates. A YOLOv11 segmentation model produces pixel-level marking masks, and LiDAR candidates are projected onto the image plane for semantic confirmation. Confirmed points are accumulated into grid cells and evaluated using reflectance, point density, fusion retention, and geometric coverage indicators. In a representative route, 2441 grid units were analyzed: 1210 units were valid for formal grading, with 1060 good, 131 slightly worn, and 19 moderately worn units; 1231 units were reserved for review. The mean composite score of the valid grids was 0.906. A five-report aggregate further showed that 87.2% of the units received valid wear categories. The results indicate that multimodal fusion transforms road marking inspection into a quantitative, spatially referenced, and reportable process.