  Convolutional neural networks (CNNs) and Transformers have shown advanced
accuracy in crack detection under certain conditions. Yet, the fixed local
attention can compromise the generalisation of CNNs, and the quadratic
complexity of the global self-attention restricts the practical deployment of
Transformers. Given the emergence of the new-generation architecture of Mamba,
this paper proposes a Vision Mamba (VMamba)-based framework for crack
segmentation on concrete, asphalt, and masonry surfaces, with high accuracy,
generalisation, and less computational complexity. Having 15.6% - 74.5% fewer
parameters, the encoder-decoder network integrated with VMamba could obtain up
to 2.8% higher mDS than representative CNN-based models while showing about the
same performance as Transformer-based models. Moreover, the VMamba-based
encoder-decoder network could process high-resolution image input with up to
90.6% lower floating-point operations.
