Image Segmentation for Pavement Crack Detection System

Pavement distress refers to the condition of pavement surface in terms of its general appearance. Cracks is a type of pavement distress and commonly occur in a road infrastructure. Crack on a pavement surface shows an early sign of pavement problems and aging. Thus, it is important to detect a serious crack as soon as possible to avoid any road accident that might occur. This study shows a comparison of three popular methods of image segmentation; watershed, k-means clustering and Otsu thresholding for pavement crack detection system in terms of it overall performance. Sample of crack images from three different types of crack such as transverse, longitudinal and crocodile crack are captured manually using digital camera and from online sources. The image is then imported into MATLAB software where it will be compressed but without reducing its quality and pixels intensity. The compressed image is then converted into grayscale to make it easier for analyzing as the system only need to work with one layer instead of three layers (RGB). The contrast of the image is then stretched to increase the level of contrast between the crack and the background. Then, the image will be segmented using three different segmentation method that are mentioned above. Lastly, morphological operation is used to reduce the noise from the image segmented. The result of the segmented image will be analyzed in term of its Structural Similarity Index (SSIM) and Mean Squared Error (MSE). The performance of the system is measure using images with a high level of contrast between the crack and the surface and images with a low level of contrast between the crack and the surface.