Identification Algorithm for Asphalt Pavement Cracks Based on Support Vector Machine

Abstract

Pavement cracks are the main manifestations of the asphalt pavement distress for high-grade highways. Pavement cracks will not only affect the road appearance and the comfort of driving, but also easily lead to structural damage to pavement, hence shorten the life of road. The type of cracks varies according to the cause of distress. To some extent, the type of cracks shows the extent of pavement damage. The identification of the type of cracks plays an important role in exploring the causes of distress, detecting the extent of pavement damage, and finding a way to prevent the distress. In this paper, based on a support vector machine, an identification algorithm of detecting the horizontal, longitudinal and net cracks of asphalt pavement is proposed. According to the geometry of different cracks, the authors use the number of pixels, the aspect ratio, the level projection, and the vertical projection of the connected area of the cracks, to design a classifier based on the support vector machine; and to identify the type of cracks. The experimental results show that the rates of correctly identifying different types of cracks by using the identification algorithm based on the support vector machine are as follows: 96.3% for the horizontal cracks, 97.2% for the longitudinal cracks, and 94.3% for the net cracks.