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논문 기본 정보

자료유형
학술저널
저자정보
Hoon Sohn (Korean Advanced Institute for Science and Technology) Jun Lee (Korean Advanced Institute for Science and Technology) Kiyoung Kim (Korean Advanced Institute for Science and Technology) Hyeonjin Kim (Korean Advanced Institute for Science and Technology)
저널정보
국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal 제33권 제1호
발행연도
2024.1
수록면
55 - 64 (10page)

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초록· 키워드

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This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zeroshot 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 chargecoupled 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%.

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