@inproceedings{qiCrackSegMambaLightweightMamba2024,
  title      = {{{CrackSegMamba}}: {{A Lightweight Mamba Model}} for {{Crack Segmentation}}},
  shorttitle = {{{CrackSegMamba}}},
  booktitle  = {2024 {{IEEE International Conference}} on {{Robotics}} and {{Biomimetics}} ({{ROBIO}})},
  author     = {Qi, Weiqing and Ma, Fulong and Zhao, Guoyang and Liu, Ming and Ma, Jun},
  year       = 2024,
  month      = dec,
  pages      = {601--607},
  issn       = {2994-3574},
  doi        = {10.1109/ROBIO64047.2024.10907574},
  urldate    = {2025-12-12},
  abstract   = {Crack localization and segmentation are essential for infrastructure maintenance and safety assessments, enabling timely repairs and preventing structural failures. Despite advancements in deep learning, crack segmentation remains challenging due to the need for real-time performance and computational efficiency. Existing methods often rely on large, resource-intensive models, limiting their practical deployment. We introduce CrackSegMamba, a novel model featuring Channel-wise Parallel Mamba (CPM) Modules, which achieves state-of-the-art performance with fewer than 0.23 million parameters and just 0.7 GFLOPs. CrackSegMamba reduces computational cost by 40-fold and parameter count by nearly 100-fold compared to existing models, while maintaining comparable accuracy. These features make CrackSegMamba ideal for real-time applications. Additionally, we present Crack20000, an annotated dataset of 20,000 concrete crack images to support further research and validation. Evaluations on the Crack500 [1] and Crack20000 datasets demonstrate that CrackSegMamba delivers comparable accuracy to leading methods, with significantly reduced computational requirements. Project page is available at: https://sites.google.com/view/cracksegmamba.},
  langid     = {american},
  keywords   = {Accuracy,Computational efficiency,Computational modeling,Location awareness,Maintenance,Maintenance engineering,Real-time systems,Robots,Robustness,Safety},
  file       = {C:\Users\Dustella\Zotero\storage\C5CSPLGW\Qi et al. - 2024 - CrackSegMamba A Lightweight Mamba Model for Crack Segmentation.pdf}
}

@misc{PDFFeaturePyramid,
  title        = {({{PDF}}) {{Feature Pyramid}} and {{Hierarchical Boosting Network}} for {{Pavement Crack Detection}}},
  journal      = {ResearchGate},
  urldate      = {2025-12-13},
  abstract     = {PDF \textbar{} Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic... \textbar{} Find, read and cite all the research you need on ResearchGate},
  howpublished = {https://www.researchgate.net/publication/330244656\_Feature\_Pyramid\_and\_Hierarchical\_Boosting\_Network\_for\_Pavement\_Crack\_Detection},
  langid       = {english},
  file         = {C:\Users\Dustella\Zotero\storage\SESVZLX5\330244656_Feature_Pyramid_and_Hierarchical_Boosting_Network_for_Pavement_Crack_Detection.html}
}
