Image-Based Crack Detection Using Sub-image Technique

Automated detection of asphalt pavement distresses is a very popular computer vision and image processing problem. In recent years, automated detection is an essential part of every pavement management system, since it allows very fast detection of distresses on the road. This is important because timely detection can prevent many road accidents, and hence it has potential to save lives. In this paper, we presented a new unsupervised image processing method for segmentation of the most common road distresses-pavement cracks. The method first performs slicing of an image into M×N sub-images, and then removes sub-images without cracks based on empirically defined threshold. Analysis is then carried out only on a small number of sub-images, which significantly reduces computation time. Then, a series of images processing tasks are performed to select only pixels with pavement cracks. The method is suitable as a pre-processing step in a number of computer vision tasks, and can provide rough estimation of damaged area in an image.