Pavement crack detection using Otsu thresholding for image segmentation
Pavement cracks are the first signs of structural damage in the asphalt pavement surfaces. The oldest method for detection and estimation of the pavement cracks is human visual inspection, also known as manual visual inspection. However, using human inspectors is very time consuming, very expensive and poses a risk to human safety. Another negative side is the fact that the task generally requires road to be closed. Hence, automatic prevention and reparation of cracks on the asphalt surface pavements is an important task, especially because the advanced stages of road deformation lead to formation of potholes. This has negative impact on the total reparation cost. In this paper, we proposed a new unsupervised method for the detection of cracks with gray color based histogram and Ostu's thresholding method on 2D pavement image. At first, the method divides the input image into a four independent equally sized sub-images. Then, the search for cracks is based on the ratio between Ostu's threshold and the maximum histogram value for every sub-image. Finally, all sub-images are assembled into the resulting image. The method was tested on the dataset which contains different pavement images with very versatile types of cracks. The results showed that the proposed method achieves satisfactory performance, especially in the cases of low signal-to-noise ratio, and is very fast.