Segmentation algorithms for road marking digital image analysis

The road user costs accruing on road networks due to the poor quality of road markings are significant. There is therefore a need for an automated process to provide an objective assessment of the performance characteristics of road marking, such as wear and both day- and night-time visibility. Digital analysis of road surface images seems to be a versatile approach that may enable road agencies to evaluate the state of markings in an objective manner with increased accuracy. The analysis is effected by a series of processes that include image acquisition and digitisation, storage, segmentation, enhancement and feature description. This paper focuses on image segmentation, the process by which the road markings are identified from their background. The paper presents a number of techniques used for image segmentation that have been considered for inclusion in a prototype road marking analysis system. The techniques presented are the variance method, moment preserving thresholding, minimum error thresholding, relaxation thresholding, and a technique known as sub-image thresholding, which combines minimum error thresholding with a method suggested for the analysis of images of the heart. Each of the techniques is presented, discussed and evaluated for its ability to segment images containing road markings. By considering a set of appropriate test data, an investigation was carried out that indicated that the sub-image thresholding method performed satisfactorily on all images analysed in terms of accuracy and speed of computation.