Crack Detection for Concrete Architecture Images Using Feature Enhancement Filtering and Shape Guided Active Contour Model

Abstract

Accurate and automatic crack detection for concrete architecture images is quite important and challenging. A joint crack detection model is presented in this paper, integrating the nonlocal means model for the noise removing, the multi-scale Hessian filtering for line-like feature enhancement, the morphological operations for the coarse segmentation, and the localized active contour model for the fine results. Firstly, the nonlocal mean filtering is adopted to reduce the noise which appears during the acquisition of the concrete architecture image, preserving the details of the edges information simultaneously. Secondly, an improved multi-scale linear feature enhancement filtering is used to strengthen the crack target; Then, a set of morphological operations and the thresholding model are employed to ameliorate the results and output a binary image which is used to initialize the level set function and guide the evolution of the active contours. Finally, the localized active contour model integrating the intensity and the shape information is utilized to refine the coarse results. Experiments and comparisons on the crack images show the effectiveness of the proposed model.