Proximal support vector machine based pavement image classification

Abstract

Pavement cracking is one of the most important distress types. This paper provids an approach for achieving an automatic classification for pavement surface images. First, image enhancement is performed by mathematical morphological operator. secondly, pavement image segmentation is performed to separate the cracks from the background. Projection features are then extracted. The proximal support vector machine(PSVM) is used for pavement surface images classification, which is more efficient and easier to be implemented than the traditional support vector machine. The experimental results prove that the proposed method not only improves the computation efficiency but also preserves the classification performance.