Applying Machine Vision Algorithm on Pavement Marking Retroreflectivity Measurement

Abstract

This preliminary study presents the development of integrating a real-time mobile device with a machine vision algorithm to assess the retroreflectivity of the broken lane lines of in-service road marking. A stereo camera was used as the photometer, and the measuring vehicle’s headlights were used as the illumination system. The machine vision algorithm includes marking centroid determination, standard measuring condition control, illumination condition calculation, and luminance measurement. The test results show that the average absolute error percentage of 47 marking samples is 6.1%, with the highest and lowest accuracies of 99.9% and 85.7%, respectively. The left and right lane line (broken line) markings can be evaluated simultaneously in a single pass up to 100 kph. The hardware package, including a stereo camera, a camera support beam, cables, and a laptop computer, costs approximately USD 3,500, which is much lower than the cost of conventional and advanced fully automatic retroreflectometers. Moreover, the developed method is for general usage and can be easily modified and applied to camera sets and vehicle carriers of different specifications. Although the results are promising, the proposed method has some limitations. First, the accuracy decreases when the test section is rough with bumps and dips. Second, this algorithm is not ready for surveying solid lines. Furthermore, the current version can only be implemented under vehicle headlights. Future work can focus on improving the hardware and machine vision algorithm to overcome the challenges. This preliminary study aims to develop an integrated system for automatically surveying road markings’ retroreflectivity. In many countries and regions, road markings’ retroreflectivity is only examined during construction instead of being inspected periodically due to a lack of efficient equipment, budget, and techniques for surveying. Although some commercial instruments have shown promising results, no detailed information or discussion on the algorithm of reflective light acquisition and retroreflectivity computation has been reported. This study presents a detailed description about the developed machine vision algorithm that can detect and analyze the marking’s dry retroreflectivity in the nighttime. The hardware package, including a stereo camera, a camera support beam, cables, and a laptop computer, costs approximately USD 3,500, which is lower than the cost of conventional and advanced fully automatic retroreflectometers. Moreover, the developed method is for general usage and can be easily modified and applied to camera sets and vehicle carriers of different specifications. However, currently the algorithm is only for broken lane line under relatively dark environmental condition. Future studies are suggested to include more road marking types and reduce the effects of ambient light sources on the analyzed results.