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Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

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  • Qing An
  • Xijiang Chen
  • Xiaoyan Du
  • Jiewen Yang
  • Shusen Wu
  • Ya Ban
  • Ning Cai

Abstract

For a long time, cracks can appear on the surface of concrete, resulting in a number of safety problems. Traditional manual detection methods not only cost money and time but also cannot guarantee high accuracy. Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. The research results show that the proposed method not only classifies crack and noncrack efficiently but also identifies cracks in complex backgrounds. The proposed method has high accuracy in crack recognition, which is at least 97.3% and even up to 98.6%.

Suggested Citation

  • Qing An & Xijiang Chen & Xiaoyan Du & Jiewen Yang & Shusen Wu & Ya Ban & Ning Cai, 2021. "Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning," Complexity, Hindawi, vol. 2021, pages 1-15, August.
    • Handle: RePEc:hin:complx:3159968
    DOI: 10.1155/2021/3159968
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