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

Shao-Jie Wang, Ji-Kai Zhang () and Xiao-Qi Lu
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Shao-Jie Wang: School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Ji-Kai Zhang: School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Xiao-Qi Lu: School of Information Engineering, Inner Mongolia University of Technology, Hohhot 101051, China

Mathematics, 2023, vol. 11, issue 15, 1-20

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.

Keywords: intelligent transportation; road cracks; image segmentation; SparseInst algorithm; convolutional attention module (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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