Pavement markings provide roadway information necessary for safe and comfortable operation. To ensure their functionality, appropriate maintenance and inspection are important. This study develops a full-scale testbed consisting of various road design parameters including marking material types, beads types, and amount of beads. Then using the field-collected images and associated retro-reflectivity (RL), Computer Vision (CV) based analysis are performed. Parameters used for examining the pavement marking construction quality are extracted to correlate with RL. In addition, a machine learning algorithm is developed to classify the RL class (from Class I to Class IV, based on RL values). Based on the CV analysis, a marking material that resulted in a deeper embedment and bead types that were prone to scatter in the test bed were revealed. Also, the overall accuracy of 82% is achieved from a transfer learning-based model, demonstrating the potential for using CV and ML algorithms for road line visibility maintenance.