An Unsupervised Approach for Road Surface Crack Detection

Abstract

Road surface distress is one of the major concerns for safety in transportation management. Surface crack is the initial stage for the structural breakdown of the asphalt pavement which may gradually deteriorate to potholes resulting in huge reforming cost in the later stage. So, detection of road surface cracks needs a good extent of attention for avoiding these inconsistency of transportation sector. Traditional manual inspection usually performed through human visualization which requires huge amount of time. So, in order to automate this inspection, an unsupervised approach has been proposed in order to detect the pavement cracks. Therefore, a method has been proposed to detect the road surface domain on the basis of color histogram analysis of pavement surfaces. K-means clustering algorithm followed by Otsu thresholding has been done for segmentation purpose in order to detect cracks on 2D road surface image. The presented algorithm provides a satisfactory result in case of detecting and localizing the crack of an image. It can effectively remove the noise and preserve edges which is very useful to attain an accuracy of good extent.