In order to realize the design of vision-based cement crack repair robot, it is necessary to accurately recognize and extract features of cracks. In this paper, three kinds of typical crack are selected to study, which are fine crack, reticulated crack and dark crack. Firstly, image filtering and image enhancement are used to pre-process the collected image to reduce the influence of noise on detection and enhance the contrast between image background and crack area. Then, the multi-scale morphological operation is applied to extract the fracture edge features effectively. The experimental results show that the proposed edge regions are obviously different from the background regions. Furthermore, by calculating and selecting the area of the largest connected area, the noise can be eliminated to the greatest extent. Finally, the traditional skeleton extraction algorithm is improved to eliminate the number of burrs in the traditional skeleton algorithm. By remapping the cracks images to color images, it can be found that the crack recognition and skeleton extraction meet the requirements, which can provide corresponding technical support for the navigation design of the crack repair robot.