Smart Structures and Systems

  • 제33권1호
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  • Pages.55-64
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  • 2024
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Coating defect classification method for steel structures with vision-thermography imaging and zero-shot learning

  • Jun Lee (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Kiyoung Kim (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Hyeonjin Kim (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Hoon Sohn (Department of Civil Engineering, Korean Advanced Institute for Science and Technology)
  • 투고 : 2023.10.31
  • 심사 : 2023.12.10
  • 발행 : 2024.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

키워드

과제정보

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [Grant Number 2019R1A3B3067987].

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