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CiC-NET: a real-time semantic segmentation network for dam surface crack detection

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Abstract

Crack detection is vital for maintaining hydraulic engineering infrastructure. However, achieving a balance between real-time processing and high precision in semantic segmentation models presents a significant challenge, especially given the intricate details of cracks and complex backgrounds. To tackle this issue, this paper proposes a real-time, high-precision crack segmentation model. Initially, a four-branch feature extraction structure is devised to capture the edge details of cracks, with a focus on enhancing segmentation accuracy. Subsequently, an image pyramid is constructed at the input end to feed small-scale samples into high-dimensional feature extraction branches, thereby reducing computational costs and improving segmentation speed. Finally, an effective feature fusion module is designed for the feature extraction structure to capture sufficient crack features and achieve precise crack localization. Extensive experiments validate the superior performance of the proposed method, with Pixel Accuracy, Recall, Intersection over Union, and F1 score reaching 68.45\(\%\), 67.04\(\%\), 51.22\(\%\), and 67.74\(\%\), respectively, while maintaining a modest parameter count of 13.12M. This model provides a reliable and efficient solution for the health monitoring and maintenance of hydraulic engineering.

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Data Availability

Authors declare that all the data being used in the design and production cum layout of the manuscript is declared in the manuscript.

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Acknowledgements

The authors express their gratitude to all researchers for their invaluable contributions to the field of machine vision, which have greatly enriched the development of this paper. Special thanks are extended to the Robot Technology Used for Special Environment Key Laboratory of Sichuan Province for their generous financial support, which has played a crucial role in the realization of this research. Additionally, the authors would like to acknowledge the support received from the Key Research and Development Program of Heilongjiang (No. 2023ZX01A18), which provided funding for the research presented in this paper.

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Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, China

    Linjing Li, Hao Zhao, Ran Liu, Rashid Ali & Hua Zhang

  2. University of Science and Technology of China, Heifei, China

    Hao Zhao

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Ran Liu

  4. School of Computer Science, Duy Tan University, Da Nang, 550000, Vietnam

    Anand Nayyar

  5. Department of Computer Science, University of Turbat, Turbat, Pakistan

    Rashid Ali

  6. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Yonglong Li

  7. Tianfu Institute of Research and Innovation, Southwest University of Science and Technology, Chengdu, China

    Hua Zhang

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  1. Linjing Li
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Correspondence to Anand Nayyar.

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Li, L., Zhao, H., Liu, R. et al. CiC-NET: a real-time semantic segmentation network for dam surface crack detection. Multimed Tools Appl 84, 27925–27947 (2025). https://doi.org/10.1007/s11042-024-20208-9

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