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Research on Real-Time Detection Algorithm for Pavement Cracks Based on SparseInst-CDSM

Author

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  • Shao-Jie Wang

    (School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    Key Laboratory of Pattern Recognition and Intelligent Image Processing in Inner Mongolia Autonomous Region, Baotou 014010, China)

  • Ji-Kai Zhang

    (School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    Key Laboratory of Pattern Recognition and Intelligent Image Processing in Inner Mongolia Autonomous Region, Baotou 014010, China)

  • Xiao-Qi Lu

    (School of Information Engineering, Inner Mongolia University of Technology, Hohhot 101051, China)

Abstract

This paper proposes a road crack detection algorithm based on an improved SparseInst network, called the SparseInst-CDSM algorithm, aimed at solving the problems of low recognition accuracy and poor real-time detection of existing algorithms. The algorithm introduces the CBAM module, DCNv2 convolution, SPM strip pooling module, MPM mixed pooling module, etc., effectively improving the integrity and accuracy of crack recognition. At the same time, the central axis skeleton of the crack is extracted using the central axis method, and the length and maximum width of the crack are calculated. In the experimental comparison under the self-built crack dataset, SparseInst-CDSM has an accuracy of 93.66%, a precision of 67.35%, a recall of 66.72%, and an IoU of 84.74%, all higher than mainstream segmentation models such as Mask-RCNN and SOLO that were compared, reflecting the superiority of the algorithm proposed in this paper. The comparison results of actual measurements show that the algorithm error is within 10%, indicating that it has high effectiveness and practicality.

Suggested Citation

  • Shao-Jie Wang & Ji-Kai Zhang & Xiao-Qi Lu, 2023. "Research on Real-Time Detection Algorithm for Pavement Cracks Based on SparseInst-CDSM," Mathematics, MDPI, vol. 11(15), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3277-:d:1202550
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    References listed on IDEAS

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    1. Xiaoran Feng & Liyang Xiao & Wei Li & Lili Pei & Zhaoyun Sun & Zhidan Ma & Hao Shen & Huyan Ju, 2020. "Pavement Crack Detection and Segmentation Method Based on Improved Deep Learning Fusion Model," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-22, December.
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    Cited by:

    1. Nizar Faisal Alkayem & Ali Mayya & Lei Shen & Xin Zhang & Panagiotis G. Asteris & Qiang Wang & Maosen Cao, 2024. "Co-CrackSegment: A New Collaborative Deep Learning Framework for Pixel-Level Semantic Segmentation of Concrete Cracks," Mathematics, MDPI, vol. 12(19), pages 1-37, October.

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