Abstract
The retroreflectivity (Rl) of road markings is important and should be inspected and maintained throughout their service life. The specifications are provided by European nations, the United States, and many other countries. Although acceptance tests ensure the good Rl quality of newly placed road markings, the RL values of all in-service road markings are rather difficult to inspect by using currently available devices. This study, therefore, aims to determine the relationship between Rl and corresponding image brightness of yellow road markings to evaluate their visibility by analyzing recorded images captured at night. An integrated algorithm was developed to analyze recorded images continuously for identifying road marking brightness 30 m away from a vehicle. Field experiments on three types of road marking materials were performed and repeated at four separate locations. The findings provide a promising direction for using the image brightness of road markings to predict their field Rl. However, limitations of this study are discussed and suggestions for future direction are presented.
Highlights
Road markings play an important role in ensuring road safety
Considering the possible positive correlation between the concepts of road marking RL and image characteristics, this study aims to study the feasibility of using road marking image brightness captured from video recording devices to analyze RL and increase the efficiency of the RL evaluating of in-service road markings
The mask R-Convolutional neural networks (CNNs) method provides good results for identifying road marking images, which are retained for further analysis
Summary
Road markings play an important role in ensuring road safety. the retroreflectivity (RL) and completeness of road markings should be maintained to an acceptable level throughout their entire service life to fulfill their functions. The United States Manual on Uniform Traffic Control Devices (MUTCD) specifies the lowest maintained levels of dry nighttime visibility RL of 50 and 100 mcd/m2/lx for yellow road markings in roadways with speed limits of 35 to 50 and above 50 mph, respectively [1]. European nations laid down specifications CEN 1436 for yellow road marking RL, with the minimum levels of R1 (the lowest requirement) and R4 (the highest standard) set at 80 and 200 mcd/m2/lx, respectively [5]. Devices have been developed to measure road marking RL, and the commonly used measuring devices include two general types: handheld and mobile retroreflectometers The former is usually handled manually to measure road marking RL from one location to another or mounted with moving wheels and pushed manually at a walking speed. Considering the possible positive correlation between the concepts of road marking RL and image characteristics, this study aims to study the feasibility of using road marking image brightness captured from video recording devices to analyze RL and increase the efficiency of the RL evaluating of in-service road markings
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