2023 Vol.6 Aug.N04

• Title: Machine vision-based crack detection method for concrete bridge bottom surface
• Name: Jian Shen,Qinghua Fan
• Company: Jilin Communications Polytechnic,Changchun,130012 China
• Abstract:

  The current conventional concrete bridge bottom crack detection method mainly learns the crack image features to identify the crack image, which leads to poor detection accuracy due to the lack of refinement segmentation processing of the crack image. In this regard, a crack detection method based on machine vision for concrete bridge underside is proposed. UAV technology is used to acquire the bridge bottom crack image data, and the pixel parameters are converted into actual parameters by designing a camera calibration scheme. And the STING grid clustering algorithm is used to determine the crack area and perform grid segmentation on the crack images. Finally, the location distribution of bridge cracks in the images is standardized by dividing the crack images into grids of the same size. In the experiments, the proposed method is verified for detection accuracy. The experimental results show that the algorithm has a higher FI value and possesses a more excellent detection accuracy when the proposed method is used to detect the cracks on the bottom surface of concrete bridges.

  
  

• Keyword: machine vision; concrete bridges; crack detection;
• DOI: 10.12250/jpciams2023090608
• Citation form: Jian Shen.Machine vision-based crack detection method for concrete bridge bottom surface [J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.35-40

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