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November 28, 2025Scientific ReportsOpen Access

A lightweight crack segmentation network based on the importance-enhanced Mamba model

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Authors

YWYunfeng WangJJJie JinXCXiong Chen

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Overview

This network improves segmentation accuracy and reduces computational complexity in road infrastructure maintenance.

Key Points

  • Segmentation accuracy improves significantly while reducing computational complexity.
  • The model effectively integrates feature extraction methods for better performance.
  • Assessment using public datasets demonstrates improved outcomes over traditional approaches.
  • Research indicates enhancements may prolong the service life of transportation infrastructure.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/6928f106a65b730b9ea79ae9https://doi.org/10.1038/s41598-025-25504-4
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A lightweight crack segmentation network based on the importance-enhanced Mamba model2025 · 3 citations
  2. 2A Lightweight CNN–Mamba Hybrid Architecture for Efficient Crack Segmentation2026
  3. 3Lightweight CNN–Mamba Hybrid Network for Multi-Scale Concrete Crack Segmentation Using Vision Sensors2026 · 1 citations
  4. 4GeoMamba: Geometric-Prior-Infused Multi-Scale Deformable Visual Mamba for Crack Semantic Segmentation2026
  5. 5LiteCrackSeg: A lightweight hybrid CNN-transformer for efficient crack segmentation.2026 · 2 citations