Dual-branch crack segmentation network with multi-shape kernel based on convolutional neural network and Mamba

Abstract

Cracks are one of the most common pavement diseases. If not promptly repaired, they will hasten the deterioration of the road. Semantic segmentation is the most convenient pavement crack detection method to assess the damage level. Convolutional neural networks (CNN) excel at extracting local spatial information, but they have limitations in capturing global contextual information. Therefore, a dual-branch crack segmentation network (DBCNet) with Mamba and multi-shape convolutional kernels is proposed. First, a dual-branch encoder is employed to extract both spatial and contextual information, consisting of the spatial branch and the context branch. The cross-like block (CrossBlock) that excels in extracting spatial information horizontally and vertically from cracks is proposed. Multiple CrossBlocks are stacked to construct a lightweight network as a spatial branch. The improved Visual State Space Model (VMamba) serves as a context branch for modeling long-range dependencies for more accurate pixel-by-pixel segmentation. Second, the Feature Fusion Module (FFM), based on squeeze-and-excitation attention, is constructed to dynamically fuse the features from the two branches layer by layer. Third, a Cross-aware Mamba Module (CMM) with the hybrid CNN-Mamba architecture is proposed to compose the decoder. Fourth, comprehensive evaluations were conducted on three public datasets. Performs on multiple metrics achieved considerable progress, outperforming the seven state-of-the-art models. The mean intersection over union (mIoU) on Deepcrack, CrackTree 260, and CFD reached 87.87%, 85.34%, and 81.35%, respectively. Code and data will be available at https://github.com/name191/DBCNet.