Computers and Concrete

  • 제34권5호
  • /
  • Pages.535-546
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  • 2024
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of concrete slump by RGB-D image feature fusion

  • Huansen Chen (College of Mechanical Engineering and Automation, Huaqiao University) ;
  • Jianhong Yang (College of Mechanical Engineering and Automation, Huaqiao University) ;
  • Huaiying Fang (College of Mechanical Engineering and Automation, Huaqiao University) ;
  • Shaojie Wu (College of Mechanical Engineering and Automation, Huaqiao University) ;
  • Bohong Lin (College of Mechanical Engineering and Automation, Huaqiao University)
  • 투고 : 2023.08.21
  • 심사 : 2024.02.26
  • 발행 : 2024.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

Slump is an important index for concrete fluidity, which has a direct guiding effect on construction. In recent years, using RGB images for evaluating slump has been confirmed by scholars. Based on previous studies, this paper investigates the superiority of RGB-D image data over RGB image data in predicting slump of concrete and proposes three RGB-D fusion models: The early-stage-fusion model performs feature fusion in the data input stage, while the fully-connected-layer-fusion model performs feature fusion in the classification layer and the middle-stage-fusion model performs feature fusion after each residual block. In the classification of slump 120 mm, 150 mm and 200 mm, the Precision, Recall and F1-score are used to evaluate the model's ability to classify a single class, and the Accuracy, Macro-F1, Kappa and MCC are used to evaluate the model's performance. The experimental results showed that compared with the model using only RGB images, the fusion model achieve better performance, indicating that RGB-D image data can better evaluate concrete slump.

키워드

과제정보

This research is financially supported by the Major Program of Industry and University Cooperation of Fujian Province (2024H6010) and Quanzhou Introduces High-level Talent Team Program (2023CT003).

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