Topology-aware Mamba for Crack Segmentation in Structures

TL;DR

CrackMamba is a novel topology-aware deep learning model that improves crack segmentation accuracy and efficiency in infrastructure monitoring by integrating specialized modules and a new decoder architecture.

Contribution

It introduces the VMambaV2 encoder, Snake Scan, and SCVSS blocks, offering a new approach that balances accuracy and computational efficiency for crack segmentation tasks.

Findings

Achieves state-of-the-art results on CrackSeg9k and SewerCrack datasets.

Demonstrates strong generalization on retinal vessel segmentation.

Outperforms traditional CNN and ViT models in accuracy and efficiency.

Abstract

CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are computationally intensive. CrackMamba addresses these challenges by utilizing the VMambaV2 with pre-trained ImageNet-1k weights as the encoder and a newly designed decoder for better performance. To handle the random and complex nature of crack development, a Snake Scan module is proposed to reshape crack feature sequences, enhancing feature extraction. Additionally, the three-branch Snake Conv VSS (SCVSS) block is proposed to target cracks more effectively. Experiments show that CrackMamba achieves state-of-the-art (SOTA) performance on the CrackSeg9k and SewerCrack datasets, and demonstrates competitive performance on the retinal vessel segmentation dataset CHASE\underline{~}DB1, highlighting its generalization capability. The code is publicly available at: {https://github.com/shengyu27/CrackMamba.}