Crack detection in building structures is an important approach to evaluating the safety of these structures. However, in some cases under special circumstances, it is very difficult to identify all features of cracks by visual inspection. To tackle this problem and offer a higher potential for practical implementations, developing an automatic crack detection method seems essential. This paper establishes a novel algorithm for surface crack detection in building walls based on image processing techniques to enhance crack visibility. One of the most prevalent issues in surface crack detection from digital crack images is non-uniform illumination of background which makes narrow and tiny cracks indistinguishable. In this regard, an integrated approach, combining adaptive image threshold using local first-order statistics, contrast-limited adaptive histogram equalization, and noise filtering using nonlinear diffusion filtering, is proposed to extract the whole skeleton of the crack from digital crack images with non-uniform illumination. Several realistic crack images, suffering from noise and uneven lighting of background, are processed through the proposed algorithm to demonstrate the robustness and high accuracy of the method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.