Semantic Segmentation and 3D Reconstruction of Concrete Cracks

Abstract

Damage assessment of concrete structures is necessary to prevent disasters and ensure the safety of infrastructure such as buildings, sidewalks, dams, and bridges. Cracks are among the most prominent damage types in such structures. In this paper, a solution is proposed for identifying and modeling cracks in concrete structures using a stereo camera. First, crack pixels are identified using deep learning-based semantic segmentation networks trained on a custom dataset. Various techniques for improving the accuracy of these networks are implemented and evaluated. Second, modifications are applied to the stereo camera’s calibration model to ensure accurate estimation of the systematic errors and the orientations of the cameras. Finally, two 3D reconstruction methods are proposed, one of which is based on detecting the dominant structural plane surrounding the crack, while the second method focuses on stereo inference. The experiments performed on close-range images of complex and challenging scenes show that structural cracks can be identified with a precision of 96% and recall of 85%. In addition, an accurate 3D replica of cracks can be produced with an accuracy higher than 1 mm, from which the cracks’ size and other geometric features can be deduced.