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A Lightweight CNN–Mamba Hybrid Architecture for Efficient Crack Segmentation

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Randomized trial demonstrates improved computational efficiency in pavement crack segmentation, suggesting practical applications.

This research aims to develop a lightweight CNN-Mamba hybrid architecture for efficient pavement crack segmentation.
Proposed a hybrid architecture replacing MobileViT modules with EfficientViM-inspired blocks.
Refined segmentation with DCNv2-based deformable convolution.
Conducted experiments on the GAPs384 and CamCrack789 datasets to evaluate performance.
Increased inference speed from 1.49 to 4.44 FPS on GAPs384 and from 1.32 to 3.92 FPS on CamCrack789.
Reduced peak memory consumption from 2827 MB to 355 MB on both datasets.
Maintained clDice scores of 0.760 to 0.758 on GAPs384 and from 0.921 to 0.922 on CamCrack789.

SHIMASAKI et al. (2026) studied this question.

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View Full Paper

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Authors

Randomized trial demonstrates improved computational efficiency in pavement crack segmentation, suggesting practical applications.

This research aims to develop a lightweight CNN-Mamba hybrid architecture for efficient pavement crack segmentation.
Proposed a hybrid architecture replacing MobileViT modules with EfficientViM-inspired blocks.
Refined segmentation with DCNv2-based deformable convolution.
Conducted experiments on the GAPs384 and CamCrack789 datasets to evaluate performance.
Increased inference speed from 1.49 to 4.44 FPS on GAPs384 and from 1.32 to 3.92 FPS on CamCrack789.
Reduced peak memory consumption from 2827 MB to 355 MB on both datasets.
Maintained clDice scores of 0.760 to 0.758 on GAPs384 and from 0.921 to 0.922 on CamCrack789.

SHIMASAKI et al. (2026) studied this question.

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