Concrete crack segmentation based on multi-dimensional structure information fusion-based network

Abstract

The state-of-the-art (SOTA) crack segmentation methods still face the challenges in low-contrast shallow crack recognition and multi-scene compatibility. Therefore, this paper proposes a solution based on a multi-dimensional structure information fusion-based network (MDSIFNet). This network consists of two branches, one for extracting two-dimensional (2D) spatial feature information and another for acquiring three-dimensional (3D) geometric one. In the former, the designed 2D curve structure constraint module based on prior knowledge combined with a pretrained 2D feature extraction module can reduce learning samples and realize crack segmentation in unseen scenes. In the latter, a one-hot transformation and a 3D geometric structure information extraction module are designed to make the network pay attention to the geometric features of shallow cracks and improve their segmentation accuracy. Finally, a fusion module couples the multi-dimensional structure feature information of these two branches and outputs pixel-wise segmentation results that can be used for crack measurement and quantitative analysis. The experimental results show that the designed model network MDSIFNet performs better than the SOTA methods in terms of performance and visualized results.