Two-Stream Boundary-Aware Neural Network for Concrete Crack Segmentation and Quantification

Abstract

Cracks can be important performance indicators for determining damage processes in new and existing concrete structures. In recent years, deep convolutional neural networks (CNNs) have shown great potential in automatic crack detection and segmentation. However, most of the current CNNs tend to lose high-resolution details and, therefore, lead to blurry object boundaries; this results in poor performance for crack images with complex backgrounds in engineering structures. This study proposes a two-stream boundary-aware crack segmentation (BACS) network that combines semantic image segmentation with semantically informed edge detection explicitly. Firstly, a high-resolution network (HRNet) is utilized in the segmentation branch for strong high-resolution representations through repeatedly conducting multi-scale fusions across parallel convolutions. Furthermore, an edge branch is utilized for preserving fine-grained details of elongated thin cracks, which adopts a modified dynamic feature fusion (DFF) network to produce more accurate and sharper edge predictions. The proposed method is evaluated using a dataset of 1,892 images for three different scenarios. The results show that the mean intersection-over-union (mIoU) scores reach 79.26%, 68.74%, and 70.31% for pure crack, complex background, and variable-width scenarios, respectively. In addition, crack width quantification is performed to validate the accuracy in terms of engineering practice. The BACS achieves high accuracy with an average absolute error of 0.0992 mm, which corresponds to approximately two pixels in the images. In conclusion, the study provides an effective solution for the crack segmentation task, especially for the variable-width scenario, providing an accurate data foundation for the digital twin of concrete structures.